WO2019200533A1 - Image processing method and apparatus and electronic device - Google Patents

Abstract

The present application provides an image processing method and apparatus, a device, and a storage medium. The method comprises: determining a first linear relationship between the output pixel value of a pixel point and the intensity of incident light , and a second linear relationship between a test pixel value and the pattern depth; acquiring the fingerprint pixel value of a pixel point when a finger is pressing thereon, and obtaining a reference pixel value in combination with the second linear relationship, and calibrating the fingerprint pixel value according to the first linear relationship. The present application can improve the accuracy of fingerprint recognition.

Description

Image processing method, device and electronic device Technical field

The embodiments of the present application relate to identification technologies, and in particular, to an image processing method, apparatus, and electronic device.

Background technique

With the development of electronic technology, the display screen of electronic devices has gradually developed to a comprehensive screen, and the higher and higher screen ratio, which makes the current mainstream capacitive fingerprint module nowhere to be placed.

The on-screen optical fingerprinting scheme can place the fingerprint sensor on the back of the display screen without occupying the area of the non-display area, thereby effectively increasing the screen ratio. In the screen optical fingerprinting scheme, the light with the fingerprint information needs to penetrate the display screen to reach the fingerprint sensor, form a fingerprint image on the surface of the fingerprint sensor, and then perform fingerprint recognition based on the fingerprint image.

However, the light-transmitting display screen with fingerprint information necessarily carries the pattern information of each layer stack inside the display screen, and the pattern information interferes with the fingerprint image, thereby affecting the accuracy and accuracy of fingerprint recognition.

Summary of the invention

The embodiment of the present application provides an image processing method, device, and electronic device to improve the definition of a target fingerprint image and improve the accuracy and accuracy of fingerprint recognition.

The embodiment of the present application provides an image processing method, which is applied to an electronic device with fingerprint recognition, the electronic device includes: a display screen and a fingerprint sensor located under the display screen; the fingerprint sensor includes a plurality of pixel points, The method includes:

Determining a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity;

Determining a second linear relationship between the test pixel value of each pixel and the depth of the pattern;

When the finger is pressed, collecting the fingerprint pixel value output by the pixel at a preset light intensity;

Determining a pattern depth of the fingerprint pixel value;

Performing calibration on the pattern depth of the fingerprint pixel value according to the second linear relationship to obtain a reference pixel value;

And correcting the fingerprint pixel value according to the reference pixel value and the first linear relationship to obtain a fingerprint image.

The embodiment of the present application may further provide an image processing apparatus, which is applied to an electronic device having fingerprint identification, the electronic device including: a display screen and a fingerprint sensor located below the display screen; the fingerprint sensor includes multiple Pixels, the device includes:

An acquisition module, configured to collect a fingerprint pixel value output by each pixel of the fingerprint sensor at a preset light intensity when the finger is pressed;

a determining module, configured to determine a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity, and a second linearity between the test pixel value and the pattern depth of each pixel Relationship, and determining a pattern depth of the fingerprint pixel value;

a calibration module, configured to calibrate a pattern depth of the fingerprint pixel value according to the second linear relationship to obtain a reference pixel value; and to use the fingerprint pixel value according to the reference pixel value and the first linear relationship Calibrate to get a fingerprint image.

The embodiment of the present application may further provide an electronic device, where the electronic device includes: a display screen and a fingerprint sensor located below the display screen; the fingerprint sensor includes a plurality of pixel points, and the electronic device further includes: a memory And a processor; the display screen, the fingerprint sensor, and the memory are respectively connected to the processor through a bus;

The memory is configured to store program instructions;

The processor is configured to execute the image processing method when the program instruction stored in the memory is invoked

The embodiment of the present application further provides a computer readable storage medium, where the storage medium stores a computer program, and the computer program is implemented by a processor to implement the image processing method.

The image processing method, device and electronic device provided by the embodiments of the present application may determine a first linear relationship between an output pixel value of each pixel point of the fingerprint sensor and an incident light intensity, and determine a test pixel of each pixel point. a second linear relationship between the pattern depth of the value and the depth of the pattern. When the finger is pressed, the fingerprint pixel value outputted by the pixel at the preset light intensity is also collected, and the pattern depth of the fingerprint pixel value is determined, and according to The second linear relationship calibrates the pattern depth of the fingerprint pixel value to obtain a reference pixel value, and then calibrates the fingerprint pixel value according to the reference pixel value and the first linear relationship to obtain a fingerprint image. In this solution, according to the determined second linear relationship between the test pixel value and the pattern depth of each pixel, the pattern depth of the fingerprint pixel value when the finger is pressed is calibrated to obtain a reference pixel value, and then based on the reference pixel value. Calibrating the fingerprint pixel value, effectively reducing the interference of the pattern information carried in the reflected light inside the screen on the fingerprint image, improving the sharpness of the target image, and improving the accuracy and accuracy of the fingerprint recognition; meanwhile, the method further determines each The first linear relationship between the output pixel value of the pixel and the incident light intensity calibrates the fingerprint pixel value, effectively reducing the sensitivity difference and the optical path difference of each pixel of the fingerprint sensor, thereby effectively reducing the sensitivity of each pixel point. Differences in the difference between the difference and the optical path, such as interference with fingerprint pixels, improve the sharpness of the fingerprint image, and improve the accuracy and accuracy of fingerprint recognition.

DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description of the drawings used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description It is a certain embodiment of the present application, and other drawings can be obtained according to the drawings without any creative labor for those skilled in the art.

1A is a schematic layer diagram of implementing an optical fingerprint on a screen in an AMOLED hard display provided by an embodiment of the present application;

1B is a schematic diagram of reflected light when a surface of an AMOLED hard display screen is not pressed by a finger according to an embodiment of the present application;

1C is a schematic diagram of reflected light when a surface of an AMOLED hard display screen is pressed by a finger according to an embodiment of the present application;

2A is a flowchart of an image processing method according to an embodiment of the present application;

2B is a schematic diagram of photographic performance and optical path difference of each pixel in a fingerprint sensor according to an embodiment of the present disclosure;

2C is a schematic layer diagram of collecting pixels when a test unit is pressed in an AMOLED hard display according to an embodiment of the present disclosure;

3A is a flowchart of another image processing method according to an embodiment of the present application;

FIG. 3B is a schematic diagram of image information strength according to an embodiment of the present application; FIG.

4A is a flowchart of still another image processing method according to an embodiment of the present application;

4B is a schematic diagram of an image division area according to an embodiment of the present application;

4C is a schematic diagram of another image division area according to an embodiment of the present application;

4D is a schematic diagram of image segmentation according to an embodiment of the present application;

FIG. 5 is a flowchart of still another image processing method according to an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present disclosure;

FIG. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present application. It is a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention applies, unless otherwise defined. The terminology used herein is for the purpose of describing particular embodiments, and is not intended to be limiting. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The features of the embodiments and examples described below can be combined with each other without conflict.

The image processing method, device, electronic device and storage medium provided by the following embodiments of the present application can be applied to any electronic device having a screen optical fingerprint recognition function, such as a smart phone, a notebook computer, a wearable device, and a home appliance. The under-screen optical fingerprint can be partially implemented in the display area of the display screen or in full screen.

The active-Matrix Organic Light Emitting Diode (AMOLED) hard display is taken as an example to illustrate the implementation of the optical fingerprint under the screen. Other types of displays such as organic light-emitting diodes (such as organic light-emitting diodes) The implementation of the Organic Light-Emitting Diode (OLED) display, liquid crystal display (LCD), etc. is identical or similar to that of the AMOLED hard display, and will not be described here.

FIG. 1A is a schematic stacked diagram of implementing an under-screen optical fingerprint in an AMOLED hard display according to an embodiment of the present application. As shown in FIG. 1A, the AMOLED hard display includes: a substrate glass, a display pixel, a sealing glass, a Touch Plane (TP) layer, and a cover glass. Various organic materials are distilled on the substrate glass to form display pixels, and then sealed by using sealing glass, and the substrate glass, the sealing glass and the laminated display driving circuit between them realize the display function of the display screen. The upper surface of the sealing glass is coated with a conductive material to form a TP layer. The conductive material may be, for example, Indium Tin Oxide (ITO), metal, or the like. It should be noted that the conductive material of the TP layer can also be applied to the surface of the film, which is attached to the surface of the sealing glass. Of course, the TP layer can also be other structural forms, and details are not described herein again. The TP layer cooperates with the touch driving circuit to realize the touch function of the display screen. The TP layer is etched into various patterns. In addition, the AMOLED hard display is also provided with a circular polarizer to suppress the reflection of the display screen to ambient light, achieving higher display contrast, and the circular polarizer is located between the TP layer and the cover glass. The TP layer is bonded to the circular polarizer by a transparent optical adhesive (OCA), and the circular polarizer is bonded to the cover glass through the OCA. It should be noted that FIG. 1A is only one example of the structure of each stack in the AMOLED hard display. With the development of technology, the structure of each stack of the AMOLED hard display may be other forms. This will not be repeated here. At the same time, the on-screen optical fingerprinting scheme can also be implemented in the AMOLED flexible display screen, and the laminated structure of the AMOLED flexible display screen is similar to the structure of FIG. 1A, except that in the AMOLED flexible display screen, flexible is available due to the folding requirement. Instead of the hard glass, for example, the substrate glass shown in FIG. 1A above may be replaced with a substrate film, and the sealing glass may be replaced with a sealing film.

In an off-screen optical fingerprinting scheme based on an AMOLED hard display, a fingerprint sensor can be located below the substrate glass. The fingerprint sensor is also called an optical fingerprint sensor. The fingerprint sensor can be placed or attached to the lower surface of the substrate glass. When the finger presses on the upper surface of the cover glass, when the light source is lit, the light emitted by the light source propagates upward, and is sequentially reflected by the laminated glass, TP, OCA glue, circular polarizer, cover glass, etc., reaching the finger and being reflected. A part of the light is reflected back through each interface, and finally reaches the fingerprint sensor, so that each pixel of the fingerprint sensor outputs a corresponding pixel value, and then the pixel value outputted from each pixel point is processed to obtain a fingerprint image. The light source may be any display source such as a display pixel of an OLED display, a surface light source of an LCD display, or a light-emitting diode (LED) external to the display screen.

The reflectivity of the interface is determined by the refractive index of the material laminated on both sides of the interface. The refractive index of glass is usually 1.5, the refractive index of ITO is 1.8, the refractive index of air is 1, and the refractive index of OCA, circular polarizer, and finger is about 1.4. The vertical reflectivity of the interface between the two media can be determined based on the vertical reflectivity formula at the interface of the two layers of media. The vertical reflectance formula at the interface of the two layers of media is as follows (1):

Where R is the vertical reflectivity at the interface, and n ₁ and n ₂ represent the refractive indices of the media on both sides of the interface, respectively. According to the above formula (1), the vertical reflectance of the interface between the glass and the air can be 4%, and the vertical reflectance of the interface between the glass and the finger can be 0.12%. It should be noted that the vertical reflectance of the interface between the glass and the air may be a first value due to the difference in the material of the glass, the individual difference of the fingers, etc., and the difference between the first value and 4% may be within a preset range. The vertical reflectivity of the interface between the glass and the finger may also be a second value, and the difference between the second value and 0.12% may be within a preset range.

1B is a schematic diagram of reflected light when a surface of an AMOLED hard display screen is not pressed by a finger according to an embodiment of the present invention; FIG. 1C is a schematic diagram of reflected light when a surface of an AMOLED hard display screen is pressed by a finger according to an embodiment of the present disclosure; .

As shown in FIG. 1B, when the surface of the display screen, that is, the upper surface of the cover glass, is pressed without a finger, the light can be reflected at the interface between the air and the glass, and the upper surface of the cover glass reflects light when the finger is not pressed. For uniform light, no fingerprint information is carried, wherein the vertical reflectance at the interface between air and glass can be 4%.

As shown in Fig. 1C, when the surface of the display screen, that is, the upper surface of the cover glass, presses the finger, the finger ridge line is in contact with the glass, but air remains in the finger valley line, and the light is both in the glass and the finger ridge line. The interface reflection can also be reflected at the interface between the glass and the finger valley line (air), wherein the vertical reflectivity of the interface between the glass and the finger ridge line can be 0.12%, and the interface between the glass and the finger valley line is vertical. The reflectance can be 4%. Since the upper surface of the cover glass has a difference in reflectance between the finger ridge line and the finger valley line, the fingerprint light is carried in the reflected light on the upper surface of the cover glass when the finger is pressed. In some scenes, a screen protector (not shown in the drawing) may be attached to the cover glass, such that when the finger is pressed, the reflected light on the upper surface of the protective film may carry fingerprint information. For a display screen with a screen protector, the specific implementation of the on-screen optical fingerprinting scheme is similar to the specific implementation of the under-screen optical fingerprinting scheme of the display screen without the screen protective film, and details are not described herein again.

The reflected light on the upper surface of the cover glass and the reflected light from the finger carry fingerprint information. The stacks of light-transmitting display screens carrying fingerprint information are incident on the fingerprint sensor disposed on the lower layer of the substrate glass together with the reflected light inside the screen, and then the pixel values output by the fingerprint sensor are processed, thereby A fingerprint image is obtained, that is, fingerprint information to be extracted.

However, the light carrying the fingerprint information penetrating the display screen necessarily carries the pattern information of each layer of the display screen, especially the display pixel layer, and the reflected light inside the display screen does not carry the fingerprint information but carries the stacks inside the display screen. Based on the pattern information of the layer, the following embodiments of the present application can effectively reduce the interference of the pattern information in each layer of the display screen by calibrating the fingerprint pixel values output by the fingerprint sensor, so that the target is obtained. The image is more accurate and effectively improves the accuracy and accuracy of fingerprint recognition.

The description provided by the embodiments of the present application will be described below in conjunction with a plurality of examples.

2A is a flowchart of an image processing method according to an embodiment of the present application. The image processing method can be applied to an electronic device having fingerprint recognition, the electronic device comprising: a display screen and a fingerprint sensor. Wherein, the fingerprint sensor can be located below the display screen, and the fingerprint sensor includes a plurality of pixel points. The method may be implemented by software of the electronic device by means of software and/or hardware, or by other processors connected to the fingerprint sensor, such as a calibration processor, by software and/or hardware. The image processing method can be performed during the process of calibrating the display module of the electronic device or the electronic device before the factory or the user uses the fingerprint application process. As shown in FIG. 2, the method can include:

S201. Determine a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity.

In the method, a linear fit is performed according to the test pixel value outputted by the pixel at each of the at least one light intensity, and a first linear relationship between the output pixel value of each pixel and the incident light intensity is obtained.

其中，R _n为第n个像素点输出的像素原始数据，

为第n个像素点输出的像素原始数据与接收光强的线性斜率，

为第n个像素点的接收光强，

为第n个像素点输出的像素原始 数据与接收光强的线性截距。

和

可用于表征第n个像素点的感光性能差异。 Due to variations in the chip manufacturing process, the electrical characteristics between the pixels of the fingerprint sensor may not be exactly the same. FIG. 2B is a schematic diagram of photographic performance and optical path difference of each pixel in a fingerprint sensor according to an embodiment of the present disclosure. According to FIG. 2B, a uniform light source is placed above the fingerprint sensor, and each pixel receives light of the same light intensity, but the output raw data of the pixel is inconsistent, and the pixel raw data output by each pixel point is still linearly related to the received light intensity. The relationship between the pixel raw data outputted by the nth pixel and the received light intensity can be expressed as

Where R _n is the pixel raw data output by the nth pixel point,

The linear slope of the pixel raw data and the received light intensity output for the nth pixel,

The received light intensity for the nth pixel,

The linear intercept of the pixel raw data and the received light intensity output for the nth pixel.

with

Can be used to characterize the difference in photographic performance of the nth pixel.

其中，

为第n个像素点的接收光强，

为第n个像素点的接收光强与入射光强的线性斜率，

为第n个像素点的入射光强。 Since the illuminating pixel points and the metal traces in the display pixel layer are not completely transparent, the light above the display pixel layer passes through the display pixel layer, and part of the light is blocked. Continuing to refer to FIG. 2B, a uniform light source is placed above the display pixel layer. After the display pixel layer is partially blocked, the light intensity received by each pixel of the fingerprint sensor is inconsistent, but each pixel of the fingerprint sensor is inconsistent. The received light intensity is still linear with the incident light intensity of each pixel, and the relationship between the received light intensity of the nth pixel and the incident light intensity can be expressed as

among them,

The received light intensity for the nth pixel,

Is the linear slope of the received light intensity and incident light intensity at the nth pixel point,

The incident light intensity at the nth pixel point.

其中，

为第n个像素点输出的原始像素数据和入射光强的线性斜率，b _n为第n个像素点输出的原始像素数据与入射光强的线性截距。k _n可用于表征第n个像素点的感光性能差异和光路差异，b _n可用于表征第n个像素点的感光性能差异。 The incident light at each pixel point refers to light corresponding to each pixel point above the display pixel layer. When the incident light intensity of each pixel of the fingerprint sensor is zero, the received light intensity of each pixel of the fingerprint sensor is also zero. Therefore, the relationship between the pixel raw data outputted by the nth pixel and the incident light intensity can be expressed as

among them,

The linear slope of the original pixel data and the incident light intensity output for the nth pixel point, b _n is the linear intercept of the original pixel data output from the nth pixel point and the incident light intensity. k _n can be used to characterize the difference in photographic performance and optical path of the nth pixel, and b _n can be used to characterize the difference in photographic performance of the nth pixel.

In this embodiment, determining the first linear relationship may be determining a difference in photographic performance of each pixel of the fingerprint sensor and optical path difference information, that is, the first linear relationship may include: each pixel of the fingerprint sensor Parameter information for differences in photographic performance and optical path differences.

S202. Determine a second linear relationship between the test pixel value and the pattern depth.

In the method, the pattern depth of the test pixel value may be determined first, and then the second linear relationship is determined according to the test pixel value and the pattern depth of the test pixel value.

Since there is a linear relationship between the reflected light inside the screen corresponding to each pixel point of the fingerprint sensor and the light intensity of the light source, and the reflected light in the screen carries the pattern information, the intensity of the pattern information is also linear with the light intensity of the light source, so the pattern information The intensity is also linear with the reflected light inside the screen. Thus, in this embodiment, a linear relationship between the test pixel value and the pattern depth, that is, the second linear relationship, can be determined.

S203. Collect a fingerprint pixel value output by the pixel at a preset light intensity when the finger is pressed.

The preset light intensity may be the same as or different from the light intensity when the test pixel value is acquired.

S204. Determine a pattern depth of the fingerprint pixel value.

The pattern depth of the fingerprint pixel value may be the intensity of the pattern information carried in the fingerprint pixel value. The specific implementation of determining the pattern depth of the fingerprint pixel value in S204 in this embodiment may be similar to the specific implementation of determining the pattern depth of the test pixel value, and details are not described herein again.

S205. Calibrate the pattern depth of the fingerprint pixel value according to the second linear relationship to obtain a reference pixel value.

The preset light intensity may be different from the light intensity when the test pixel value is collected. Therefore, in order to effectively reduce the internal reflected light of the screen under the preset light intensity, the pattern information in the fingerprint pixel under the preset light intensity is reduced. The interference may be based on a linear correspondence between the test pixel value and the pattern information, that is, a second linear relationship, and the pattern depth of the fingerprint pixel value is calibrated to obtain a reference under a preset light intensity when the fingerprint pixel value is collected. a pixel value, the reference pixel value being a test pixel value outputted at a preset light intensity, which includes the internally reflected light of the screen under the preset light intensity. By subtracting the test pixel value under the preset light intensity from the fingerprint pixel value, the internal reflection light of the screen under the preset light intensity can be subtracted, and the interference of the reflected light inside the screen on the fingerprint image is eliminated.

S206. Calibrate the fingerprint pixel value according to the reference pixel value and the first linear relationship to obtain a fingerprint image.

In this embodiment, the fingerprint pixel value may be subtracted from the reference pixel value to eliminate the influence of reflected light inside the screen, and then the calibration is performed again by using the first linear relationship to reduce the sensitivity difference and optical path of each pixel of the fingerprint sensor. Differences, etc., effectively improve the clarity of the fingerprint image.

The image processing method provided by the embodiment of the present application may determine a first linear relationship between an output pixel value of each pixel point of the fingerprint sensor and an incident light intensity, and determine a second linearity between the test pixel value and the pattern depth. a relationship, when a finger is pressed, collecting a fingerprint pixel value output by the pixel at a preset light intensity, determining a pattern depth of the fingerprint pixel value, and pattern depth of the fingerprint pixel value according to the second linear relationship A calibration is performed to obtain a reference pixel value, and then the fingerprint pixel value is calibrated according to the reference pixel value and the first linear relationship to obtain a fingerprint image. In the method, according to the second linear relationship between the determined test pixel value and the pattern depth, the pattern depth of the fingerprint pixel value when the finger is pressed is calibrated to obtain a reference pixel value, and then the fingerprint pixel value is performed based on the reference pixel value. Calibration, effectively reducing the interference of the pattern information carried in the reflected light inside the screen on the fingerprint image, improving the sharpness of the fingerprint image, improving the accuracy and accuracy of fingerprint recognition; meanwhile, the method is also based on determining the output pixel of each pixel The first linear relationship between the value and the incident light intensity calibrates the fingerprint pixel value, effectively reducing the sensitivity difference and the optical path difference of each pixel of the fingerprint sensor, thereby effectively reducing the sensitivity difference and optical path difference of each pixel. The interference on the fingerprint image improves the sharpness of the fingerprint image and improves the accuracy and accuracy of fingerprint recognition.

Optionally, in the image processing method provided by the present application, when the test unit is pressed, the test pixel value outputted by each pixel at at least one light intensity is collected, and then linearly fitted according to the test pixel value, thereby obtaining the A first linear relationship between the output pixel value of each pixel and the incident light intensity.

In specific implementation, when the test unit is used for pressing, the test unit can be pressed on the upper surface of the display screen, such as the upper surface of the cover glass of the display screen, and each pixel of the fingerprint sensor is outputted under at least one light intensity. Test pixel value.

The test pixel value output by each pixel at each light intensity can be represented by pixel raw data (Rawdata) output by each pixel at each light intensity.

The test unit as referred to above may be implemented by one test unit or by two or more test units. If the test unit includes a test unit, when the test unit is pressed, it is necessary to collect test pixel values outputted by each pixel at various light intensities. If the test unit comprises a plurality of test pixels, the test pixel value of each pixel at at least one light intensity is acquired when each test unit is pressed.

Regardless of whether the test unit includes several, each test unit needs to be able to uniformly reflect the outgoing light of the light source. That is to say, the surface of each test unit is flat, which can uniformly reflect light.

In various embodiments of the present application, when the test unit is pressed, the surface of the test unit can be hollowed out, so that the test unit faces the display screen with a concave surface to effectively prevent the test unit from being covered by the display screen. Pattern interference caused by uneven contact between the plates. Moreover, when the test unit is pressed on the cover glass of the display screen, the external ambient light outside the display screen can be effectively shielded.

In one example, the test unit can include a first test unit and a second test unit.

The first test unit may be a flat test head of flesh-colored or other colors. The reflectivity of the first test unit is better than that of most of the fingers. The material of the first test unit may be silica gel or glass. Film, metal, etc., and the first test unit needs to be able to shield ambient light.

The second test unit may be a flat test head of black or other color, and the reflectivity of the second test unit is better than that of most of the fingers, since the reflectance of the first test unit may be greater than most of the fingers. The reflectivity, therefore, the reflectivity of the second test unit is also less than the reflectivity of the first test unit. The material of the second test unit may also be silica gel, glass, film, metal, etc., and the second test unit needs to be able to shield ambient light. The second test unit can be, for example, a black box, a black cover, or the like.

Since the test unit needs to shield the ambient light, it is a non-transparent test unit regardless of the first test unit or the second test unit.

In an example, determining a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity in S201 as described above may include:

When the first test unit is pressed, the first test pixel value outputted by the pixel at at least one light intensity is acquired; and when the second test unit is pressed, the pixel is collected. a second test pixel value outputted by the at least one light intensity;

And determining, according to the first test pixel value and the second test pixel value, a first linear relationship between the output pixel value of each pixel point and the incident light intensity.

The test unit shown above may also be referred to as a test probe, a test head, a test tool, and the like.

Taking an AMOLED hard display as an example, FIG. 2C is a schematic stacked diagram of collecting pixel values when a test unit is pressed in an AMOLED hard display provided by an embodiment of the present application. 2C differs from FIG. 1A described above in that, in the drawing shown in FIG. 2C, a test unit is pressed against the cover glass. When the test unit is pressed on the upper surface of the cover glass, when the light source is lit, the light emitted by the light source propagates upward, and is sequentially reflected by the laminate of sealing glass, TP layer, OCA glue, circular polarizer, cover glass, etc. to the test unit. When the light passes through each interface, it will also reflect back some of the light, and finally reach the fingerprint sensor, so that each pixel of the fingerprint sensor outputs a corresponding test pixel value. The light source may be any display source such as a display pixel of an OLED display, a surface light source of an LCD display, or a light-emitting diode (LED) external to the display screen.

At the same time, for convenience of description, all operations in the text to change the light intensity may include: changing the exposure time of the fingerprint sensor. For example, increasing the light intensity can increase the exposure time of the fingerprint sensor. If the light source is turned off, that is, the light intensity is zero, the exposure time of the fingerprint sensor can be set to zero. The above is only an example, and the present application does not limit this.

A light intensity may correspond to a light intensity gear, and the at least one light intensity may be at least one light source light intensity, which respectively correspond to different light intensity gear positions. The at least one light intensity can include two light intensities, for example, the brightness of the display screen is the brightest and the two light intensities when extinguished. The at least one light intensity when the first test unit is pressed may be the same as the at least one light intensity when the second test unit is pressed. That is to say, for different test units, the light intensity of the same gear position is the same. For example, for the first test unit, the first test pixel value under the M light intensity can be acquired, and for the second test unit, the second test pixel value under the M light intensity is also collected, when the first test unit is pressed. The mth light intensity is the same as the mth light intensity when the second test unit is pressed.

第m个光强下第n个像素点对应的盖板玻璃上表面反射光

第m个光强下第n个像素点对应的第一测试单元的反射光

因此，采用第一测试单元按压时，指纹传感器的第n个像素点在M种光强下输出的第一测试像素值可通过如下公式(2)表示。 If the first test unit is pressed above the cover glass of the display screen, the first test unit can be raised or hollowed out, and the first test unit needs to be able to shield the ambient light. When the first test unit is pressed, the incident light of the nth pixel of the fingerprint sensor at the mth light intensity may include: the internal reflection light of the screen corresponding to the nth pixel point of the mth light intensity

Reflected light on the upper surface of the cover glass corresponding to the nth pixel point at the mth light intensity

The reflected light of the first test unit corresponding to the nth pixel point at the mth light intensity

Therefore, when the first test unit is pressed, the first test pixel value outputted by the nth pixel of the fingerprint sensor under the M light intensity can be expressed by the following formula (2).

为采用第一测试单元按压时，指纹传感器的第n个像素点在第m个光强下输出的第一测试像素值，

包括第一测试单元的表面反射光和内部漫反射光，

携带有显示屏内部各叠层的图案信息，显然

和

都与光源光强成线性关系，且光源光强为零时反射光也都为零，同时

所带的图案信息强度也与光源光强成近似过零点的线性关系。 among them,

The first test pixel value output by the nth pixel of the fingerprint sensor at the mth light intensity when the first test unit is pressed,

Including the surface reflected light of the first test unit and the internal diffused light,

Carrying the pattern information of each laminate inside the display, obviously

with

Both are linear with the light source intensity, and the reflected light is also zero when the light source intensity is zero.

The intensity of the pattern information carried is also a linear relationship with the light source intensity that is approximately zero crossing.

第m个光强下第n个像素点对应的盖板玻璃上表面反射光

第m个光强下第n个像素点对应的第二测试单元的反射光

因此，采用第二测试单元被按压时，指纹传感器的第n个像素点在M种光强下输出的第二测试像素值可通过如下公式(3)表示。 If the second test unit is pressed above the cover glass of the display screen, the second test unit can also be raised or hollowed out, and the second test unit needs to be able to shield the ambient light. When the second test unit is pressed, the incident light of the nth pixel of the fingerprint sensor at the mth light intensity may include: the internal reflection light of the screen corresponding to the nth pixel point of the mth light intensity

Reflected light on the upper surface of the cover glass corresponding to the nth pixel point at the mth light intensity

The reflected light of the second test unit corresponding to the nth pixel point under the mth light intensity

Therefore, when the second test unit is pressed, the second test pixel value outputted by the nth pixel of the fingerprint sensor under the M light intensity can be expressed by the following formula (3).

为采用第二测试单元按压时，指纹传感器的第n个像素点在第m个光强下输出的第二测试像素值，

包括第二测试单元的表面反射光和内部漫反射光，

携带有显示屏内部各叠层的图案信息，显然

和

都与光源光强成线性关系，且光源光强为零时反射光也都为零，同时

所带的图案信息强度也与光源光强成近似过零点的线性关系。 among them,

The second test pixel value output by the nth pixel of the fingerprint sensor at the mth light intensity when the second test unit is pressed,

Including the surface reflected light of the second test unit and the internal diffused light,

Carrying the pattern information of each laminate inside the display, obviously

with

Both are linear with the light source intensity, and the reflected light is also zero when the light source intensity is zero.

The intensity of the pattern information carried is also a linear relationship with the light source intensity that is approximately zero crossing.

In this embodiment, after the first test pixel value and the second test pixel value are collected, the collected first test pixel value and the second test pixel value may be linearly fitted to obtain the The first linear relationship.

Alternatively, the first linear relationship as shown above may include: a first linear slope and a first linear intercept. In an implementation manner, determining, according to the first test pixel value and the second test pixel value, that the first linear relationship between the output pixel value of each pixel point and the incident light intensity may include:

Determining the first linear slope and the first linear intercept according to the first test pixel value and the second test pixel value; the first linear slope is a linearity of an output pixel value of each pixel point and an incident light intensity The slope, the first linear intercept is a linear intercept of the output pixel value of each pixel point and the incident light intensity.

In a specific implementation, when the first test unit and the second test unit are pressed under the same light intensity m, the test pixel values output by each pixel point are subtracted, that is, according to the first test pixel value and the second test. The pixel value is obtained by the following formula (4).

为第m个光强下每个像素点输出的第一测试像素值与第二测试像素值的差值。上述公式(4)中，虽然

和

未知，但

与光强分别成线性关系，它们之差也与光源光强成线性关系，且光源光强为零时，

和

之差也为零。而

和

之差的平均值也与光源光强成过零点的线性关系，因此可以将

和

之差表示

和

之差的平均值。因此，对上述公式(4)进行变换后，得到如下公式(5)。 among them,

The difference between the first test pixel value and the second test pixel value outputted for each pixel point at the mth light intensity. In the above formula (4), although

with

Unknown, but

Linearly related to the light intensity, and the difference between them is also linear with the light source intensity, and when the light source intensity is zero,

with

The difference is also zero. and

with

The average of the difference also has a linear relationship with the light source's light intensity, so it can be

with

Difference representation

with

The average of the differences. Therefore, after transforming the above formula (4), the following formula (5) is obtained.

k _n可用于表示指纹传感器各像素点的感光性能差异和光路差异，

可用于表示显示屏的盖板玻璃上表面或第一测试单元及第二测试单元脏污划痕等信息。k' _n为第一线性斜率，既可表示指纹传感器各像素点的感光性能差异和光路差异，还可表示显示屏的盖板玻璃上表面或第一测试单元及第二测试单元的脏污划痕等信息。对上述公式(5)所表示的方程组进行最小二乘法线性拟合得到k' _n和b' _n如下公式(6)所示。 among them,

k _n can be used to indicate the difference in photographic performance and optical path difference of each pixel of the fingerprint sensor.

It can be used to indicate information such as the upper surface of the cover glass of the display screen or the dirty scratches of the first test unit and the second test unit. k' _n is the first linear slope, which can represent the difference in photographic performance and optical path difference of each pixel of the fingerprint sensor, and can also represent the upper surface of the cover glass of the display screen or the dirty stain of the first test unit and the second test unit. Traces and other information. The least squares linear fitting is performed on the equations represented by the above formula (5) to obtain k' _n and b' _n as shown in the following formula (6).

b' _n can be the first linear intercept and can be used to represent the difference in photographic performance of each pixel of the fingerprint sensor.

It should be noted that in this embodiment, a linear fitting is performed by taking the least squares method as an example. Of course, other linear fitting methods may be used to obtain the first linear slope and the first linear intercept.

In still another implementation, determining a second linear relationship between the test pixel value and the pattern depth of each pixel in S202 as shown in the above may include:

Determining a pattern depth of the test pixel value, the test pixel value being the first test pixel value or the second test pixel value;

A second linear relationship between the test pixel value and the pattern depth is determined based on the test pixel value and the pattern depth of the test pixel value.

The pattern depth of the test pixel value may be the intensity of the pattern information carried in the test pixel value under the at least one light intensity, and may be used to characterize the intensity of the internally reflected light of the screen under the at least one light intensity. For example, for the pattern depth of the first test pixel value, the depth of the pattern information carried in the first test pixel value may be the at least one light intensity; and the pattern depth of the second test pixel value may be the at least one light The depth of the pattern information carried in the second test pixel value is emphasized.

Optionally, determining the pattern depth of the test pixel value in the image processing method as shown above may include:

And calibrating the test pixel value according to the first linear relationship to obtain a first intermediate image;

The pattern depth of the first intermediate image is determined.

The test pixel value is the first test pixel value or the second test pixel value.

In one example, the second test pixel value can be calibrated according to the first linear relationship to obtain a first intermediate image, and then the pattern depth of the first intermediate image is determined.

采用下述公式(7)进行校准，得到第一中间图像。 For example, the first linear relationship may include: a first linear slope k' _n and a first linear intercept b' _n determined as above. In this embodiment, according to the first linear slope k' _n and the first linear intercept b' _n , as shown in the above formula (3)

Calibration is performed using the following formula (7) to obtain a first intermediate image.

为光强m下的第一中间图像，M种光强下的第一中间图像可依次表示为

among them,

For the first intermediate image at the light intensity m, the first intermediate image under the M light intensity may be expressed as

Optionally, FIG. 3A is a flowchart of another image processing method according to an embodiment of the present application. The image processing method shown in FIG. 3A can describe how to determine the pattern depth of the first intermediate image in the above embodiment. As shown in FIG. 3A, the pattern depth of the first intermediate image is determined in the method shown above. include:

S301. Determine a pattern area mask and a non-pattern area mask of the first intermediate image.

携带有内部各叠层的图案信息(特别是TP层的图案信息)。在该实施例中，以TP层的图案信息为例对显示屏的内部反射光

受图案信息的强度影响进行说明。图3B为本申请实施例提供的一种图像信息强度的示意图。如图3B所示，屏幕内部反射光

携带的图案信息存在图案区域AreaP和非图案区域AreaNP， Due to the internal reflected light of the display

The pattern information (especially the pattern information of the TP layer) of each of the internal laminates is carried. In this embodiment, the pattern information of the TP layer is taken as an example to reflect the internal light of the display screen.

It is explained by the influence of the intensity of the pattern information. FIG. 3B is a schematic diagram of image information strength according to an embodiment of the present application. As shown in Figure 3B, the inside of the screen reflects light

The pattern information carried has a pattern area AreaP and a non-pattern area AreaNP,

S302. Determine a pattern depth of the first intermediate image according to an output pixel value of the pixel corresponding to the pattern area mask and an output pixel value of the pixel corresponding to the non-pattern area mask.

Since the intensity of the pattern information is directly characterized, the output pixel value of the pixel corresponding to the pattern area mask and the output pixel value of the pixel point corresponding to the non-pattern area mask can be processed in this embodiment, and then Information is obtained for characterizing the depth of the pattern of the first intermediate image.

In an optional implementation manner, in S302, the first pixel is determined according to an output pixel value of a pixel corresponding to the pattern area mask and an output pixel value of the pixel corresponding to the non-pattern area mask. The pattern depth of the intermediate image may include:

Obtaining a pattern depth of the first intermediate image according to a difference between an average value of the first pixel value and an average value of the second pixel value; wherein the first pixel value is an output pixel of the pixel corresponding to the pattern area mask a value, the second pixel value is an output pixel value of a pixel corresponding to the non-pattern area mask.

的平均值和非图案区域AreaNP内

的平均值的差值，得到该第一中间图像的图案深度。由于图案深度与光源光强为线性关系，光源光强越强则图案深度越深。如图3B所示，为便于算法 实现，图案区域AreaP可以用图案区域掩膜MaskP代替，MaskP可以位于AreaP内，可以等于AreaP，也可以完全覆盖AreaP。如图3B所示，为便于算法实现，非图案区域AreaNP可以用非图案区域掩膜MaskNP代替，MaskNP可以为包裹MaskP的周边区域，也可以等于AreaNP或者其他形式。 For example, the pattern area AreaP can be used.

Average and non-pattern areas within AreaNP

The difference between the average values gives the pattern depth of the first intermediate image. Since the depth of the pattern is linear with the light intensity of the light source, the stronger the light intensity of the light source, the deeper the pattern depth. As shown in FIG. 3B, in order to facilitate the implementation of the algorithm, the pattern area AreaP can be replaced by the pattern area mask MaskP, and the MaskP can be located in the AreaP, which can be equal to AreaP or completely cover AreaP. As shown in FIG. 3B, in order to facilitate the implementation of the algorithm, the non-pattern area AreaNP can be replaced by the non-pattern area mask MaskNP, which can be the surrounding area of the wrapped MaskP, or equal to AreaNP or other forms.

In order to obtain the pattern depth of the first intermediate image, the pattern mask mask algorithm is used to determine the pattern region mask MaskP of the first intermediate image and the non-pattern region mask and the non-pattern region mask MaskNP, and the pattern depth extraction algorithm is adopted. Determining the pattern depth of the first intermediate image, the pattern depth extraction algorithm extracts the pattern depth in the image according to MaskP and MaskNP, and makes the extracted pattern depth unaffected by unfavorable factors such as fingerprint, incomplete pressing or strong light.

The pattern mask lookup algorithm can have multiple implementations depending on the type of display screen. That is to say, the pattern area mask and the non-pattern area mask for determining the first intermediate image in the above S301 can be realized by various implementations.

In an implementation manner, determining the pattern area mask and the non-pattern area mask of the first intermediate image in the above S301 may include:

Determining the stack according to preset information of the layered pattern in the display screen, a preset correspondence between the pattern information and the display position on the display screen, and information of the layered pattern received by the fingerprint sensor The display position of the pattern on the display;

The pattern area mask and the non-pattern area mask are determined according to the display position.

Specifically, the information of the laminated pattern may be a priori information of the TP layer pattern, such as pattern shape, pattern line width, pattern line angle, and the like. The correspondence between the pattern information and the display position on the display screen may be, for example, a correspondence relationship between the pattern of the TP layer and the absolute position of the display area of the display screen. The information of the laminated pattern received by the fingerprint sensor may be, for example, fixed pattern information received by the fingerprint sensor, and the fixed pattern information is displayed at a fixed position of the display area of the display screen, and thus may be according to the preset laminated pattern. Determining, by the information, the corresponding correspondence between the pattern information and the display position on the display screen, and the information of the layer pattern received by the fingerprint sensor, determining a display position of the layer pattern on the display screen, and then according to the The display position estimates the pattern area mask MaskP and the non-pattern area mask MaskNP at this position. The display position of the laminate pattern on the display screen may be the absolute position of the laminate pattern on the display area of the display screen.

In another implementation manner, determining the pattern area mask and the non-pattern area mask of the first intermediate image in the above S301 may include:

The pattern area mask and the non-pattern area mask are determined according to preset information of the layered pattern in the display screen and reflected light inside the screen received by the fingerprint sensor when the light source is illuminated.

在预先获得叠层图案的信息、及该光源被点亮时该指纹传感器接收到的屏幕内部反射光的情况下，便可根据该显示屏内的叠层图案的信息、及该光源被点亮时该指纹传感器接收到的屏幕内部反射光，计算得到确定该图案区域掩膜和该非图案区域掩膜。 Specifically, the information of the laminated pattern may be a priori information of the TP layer pattern, such as pattern shape, pattern line width, pattern line angle, and the like. The reflected light inside the screen can be the reflected light inside the screen received by the fingerprint sensor when the light source is illuminated.

When the information of the laminated pattern is obtained in advance and the reflected light is received inside the screen when the light source is illuminated, the information of the laminated pattern in the display screen and the light source may be illuminated. When the fingerprint sensor receives the reflected light inside the screen, the mask of the pattern area and the mask of the non-pattern area are determined.

In order to prevent the extracted image depth of the first intermediate image from being affected by unfavorable factors such as data tilt, fingerprint, incomplete pressing or strong light, the embodiments of the present application may remove the unfavorable factors by the following implementation manners.

FIG. 4A is a flowchart of still another image processing method according to an embodiment of the present application. FIG. 4A illustrates one implementation of a pattern depth of the first intermediate image. As shown in FIG. 4A, in S302, the pattern of the first intermediate image is determined according to the output pixel value of the pixel corresponding to the pattern area mask and the output pixel value of the pixel corresponding to the non-pattern area mask. Depth can include:

S401. Perform area division on the first intermediate image to obtain a plurality of sub-areas.

In order to effectively remove the influence of data skew, fingerprint and other adverse factors on the pattern depth extraction, the first intermediate image may be divided into regions. In a specific implementation, the first intermediate image information area may be divided according to a preset sub-area size, a number of sub-areas, and the like, to implement a sub-block sub-area; and the first sub-area may be configured according to a row and/or a column. The intermediate image is divided into regions, and each row or column can be divided into a plurality of sub-regions.

S402. Determine a plurality of effective sub-regions from the plurality of sub-regions; each of the effective sub-regions includes: a partial pattern region mask and a partial non-pattern region mask.

In the method, the effective sub-regions may be determined from the plurality of sub-regions according to the type of the mask included in each sub-region, and each of the effective sub-regions includes a partial pattern region mask and a partial non-pattern region. Mask. If a sub-area includes only a pattern area mask, or a non-pattern area mask, the one sub-area is an invalid sub-area. If a sub-area does not include a pattern area mask or a non-pattern area mask, the one sub-area is also an invalid sub-area.

For example, FIG. 4B is a schematic diagram of an image division area according to an embodiment of the present application. As shown in FIG. 4B, if a partial region includes a partial pattern region mask and a partial non-pattern region mask, the one sub-region is an effective sub-region.

FIG. 4C is a schematic diagram of another image division area according to an embodiment of the present application. As shown in FIG. 4C, in a certain behavior example, the pattern area mask MaskP in this line is represented by 1, and the non-pattern area mask MaskNP is represented by 0.5. Obviously, this line can be divided into 6 effective areas.

If a sub-area includes both a partial pattern area mask and a partial non-pattern area mask, the sub-area is a valid sub-area.

S403. Determine, according to a difference between an average value of the third pixel value and an average value of the fourth pixel value, a pattern depth of each of the effective sub-regions, wherein the third pixel value is a pixel corresponding to the partial pattern region mask. An output pixel value of the dot, the fourth pixel value being an output pixel value of a pixel corresponding to the partial non-pattern area mask.

It should be noted that the S403 is only one achievable manner for determining the depth of the pattern of each effective sub-region. In another implementation manner, the intermediate value and the fourth pixel of the third pixel value may also be used. The difference in the median value in the value determines the pattern depth of each valid sub-region. Of course, the method of this embodiment may further determine a pattern depth of each valid sub-region according to a difference between other parameter values in the third pixel value and other parameter values in the fourth pixel value, where the above is only an example. This application does not limit this.

S404. Determine a pattern depth of the first intermediate image according to a pattern depth of the plurality of effective sub-regions.

Presetting the pattern depths of all the effective sub-regions to obtain the pattern depth of the first intermediate image. The preset processing may include at least one of the following: abnormal point culling, weighting, voting mechanism, averaging, and the like. Of course, the preset processing may also be other processing manners. The foregoing is only an example, and the present application does not limit this.

In the method, the first intermediate image is divided into regions to obtain a plurality of sub-regions, and an effective sub-region is selected therefrom, and the pattern depth of each sub-region is determined, and then the pattern depth is determined according to the pattern depths of the plurality of effective sub-regions. The depth of the pattern of an intermediate image can effectively avoid the influence of data skew, fingerprint and other adverse factors on the depth of the pattern, and effectively ensure the accuracy of the pattern depth, so that the fingerprint image in advance is more accurate, and the accuracy and accuracy of the fingerprint recognition are effectively ensured.

On the basis of the above embodiments, in order to make the pattern depth more accurate, the image may be first divided to obtain an effective area, and then the effective area is divided to effectively remove the data from the data such as tilt, incompleteness, and glare. The impact of depth.

For example, the first intermediate image is divided into regions in S401 as shown above, and obtaining the plurality of sub-regions may include:

Performing image segmentation on the first intermediate image to remove a partial region of the first intermediate image to obtain an effective region of the first intermediate image;

The effective area is divided into regions to obtain the plurality of sub-regions.

In a specific implementation process, for example, an image segmentation algorithm may be used to perform image segmentation on the first intermediate image to eliminate regions such as data tilt, incomplete compression, and strong light in the first intermediate image. The partial area removed from the first intermediate image may be an area in the first intermediate image in which at least one of data tilt, data incompleteness, data saturation, and the like exists. Among them, an area in which at least one of data skew, data incompleteness, data saturation, and the like may be referred to as a bad area.

FIG. 4D is a schematic diagram of image segmentation according to an embodiment of the present application. As shown in FIG. 4D, in the method, image segmentation may be performed on the first intermediate image to remove a partial region to obtain an effective region.

If there is no region of at least one of the first intermediate image, such as data skew, data incomplete, data saturation, etc., the first intermediate image has a maximum effective area, also referred to as an original effective area, which may be represented as Area _global . If the first intermediate image has an area of at least one of data skew, incomplete data, and data saturation, the effective area obtained by removing the partial area is obtained, and the sub-area of the original effective area is obtained, which may be represented as the divided effective area. Area _partition .

The description is made in the following manner, which is similar to the sub-area partitioning manner, and is not described here. The pattern depth of the effective sub-area of each row of columns is calculated in the original effective area Area _global and the divided effective area Area _partition , respectively. It is obvious that the original effective area Area _global is larger than the divided effective area Area _partition , and the respective pattern depths obtained according to the pattern depths of the effective row and sub-areas of the respective rows and columns may be different. In the mass production calibration, the pattern depth extraction algorithm can adopt the original effective area Area _global , and in the user application, the pattern depth extraction algorithm has to adopt the split effective area Area _partition due to bad factors such as data tilt, incomplete pressing, strong light, etc., so that the same The image has a difference in the depth of the image pattern obtained due to the inconsistency of the effective area, and therefore the present application can also compensate for the difference in the effective area before and after the division.

The method may comprise: a depth image treat extraction pattern, such as a fingerprint image obtained by dividing the divided effective area Area _partition, calculated pattern depth effective sub-region ranks fingerprint image within Area _partition, which in turn fingerprint image within Area _partition The pattern depth of the row and column effective sub-regions results in the segmented pattern depth FingerDepth _{partition of} the fingerprint image.

根据上述第一线性关系对

进行校准，得到

消除指纹传感器各像素点的感光性能差异和光路差异对图案深度提取的干扰。其中，

为第二测试单元被按压时，光强m下指纹传感器所有像素点输出的像素值。 Taking a preset image as a fixed image, the fixed image may be, for example,

According to the first linear relationship pair described above

Calibrate and get

Eliminate the difference in photographic performance of each pixel of the fingerprint sensor and the interference of the optical path difference on the pattern depth extraction. among them,

When the second test unit is pressed, the pixel value of all the pixel points of the fingerprint sensor under the light intensity m.

在原始有效区域Area _global内的行列有效子区域的图案深度矩阵DepthMatrix _global，继而根据原始有效区域内的行列有效子区域的图案深度矩阵DepthMatrix _global得到

在原始有效区域Area _global内的图案深度Depth _global；并且计算

在分割有效区域Area _partition内的行列有效子区域的图案深度矩阵DepthMatrix _partition，继而根据分割有效区域Area _partition内的行列有效子区域的图案深度矩阵DepthMatrix _partition确定

在分割有效区域Area _partition内的图案深度Depth _partition。 This method also needs to be calculated

DepthMatrix _global pattern depth matrix effective subregions within the ranks of the original effective area of Area _global, then the pattern obtained according to the ranks of active sub-areas within the original matrix of the effective region depth DepthMatrix _global

The depth of the pattern in the original effective area Area _global Depth _global ; and calculate

Matrix pattern depth DepthMatrix _partition active sub-areas divided in the effective region ranks Area _partition, which in turn determines the division pattern in accordance with the ranks of active sub-areas within the effective depth of matrix area Area _partition DepthMatrix _partition

The depth of the pattern Depth _partition within the area _partition of the effective area.

在原始有效区域内的图案深度Depth _global及分割有效区域内的图案深度Depth _partition的情况下，还可根据Ratio＝Depth _global/Depth _partition，得到此分割有效区域的补充系数。其中，Ratio为分割有效区域Area _partition的补偿系数。 In determining

In the case of the pattern depth Depth _global in the original effective area and the pattern depth Depth _partition in the divided effective area, the complement coefficient of the divided effective area can also be obtained according to Ratio=Depth _global /Depth _partition . Where Ratio is a compensation coefficient for dividing the effective area Area _partition .

Once the compensation coefficient is determined, the original pattern depth FingerDepth _{global of the} fingerprint image, which is also referred to as the final pattern depth of the final fingerprint image, may be determined according to the segmented pattern depth FingerDepth _{partition of} the fingerprint image and the compensation coefficient. Among them, FingerDepth _global = FingerDepth _partition *Ratio.

为例，在实际应用中还可以将

或者DepthMatrix _global进行保存，用以计算补偿系数。该固定图像可以为预设图像。 Fixed image

For example, in practical applications,

Or save it in DepthMatrix _global to calculate the compensation factor. The fixed image can be a preset image.

The method can also compensate for the difference of the pattern depth caused by the segmentation image, so that the determined pattern depth is better and more accurate, and then the accuracy of the fingerprint image is effectively ensured, and the accuracy and precision of the fingerprint recognition are improved.

The determination of the second linear relationship in the above method is also described in the embodiment of the present application. In an implementation of an example, determining a second linear relationship between the test pixel value and the pattern depth according to the test pixel value and the pattern depth of the test pixel value in the image processing method may include:

A second linear slope and a second linear intercept are determined based on the pattern depth of the first intermediate image and the first intermediate image.

The second linear relationship includes: the second linear slope and the second linear slope, the second linear slope being a linear slope of the pattern depth of the first intermediate image and the first intermediate image, the second linear The intercept is a linear intercept of the pattern depth of the first intermediate image and the first intermediate image.

为例，M种光强下的第一中间图像可依次表示为

M种光强下的第一中间图像的图像深度可依次表示为D ₁、D ₂…D _M。 First intermediate image at light intensity m

For example, the first intermediate image under M light intensity can be expressed as

The image depth of the first intermediate image under M light intensity may be sequentially expressed as D ₁ , D ₂ ... D _M .

作为线性坐标系中的Y轴，将M种光强下的第一中间图像的图像深度D ₁、D ₂…D _M作为线性坐标系的X轴，对其进行最小二乘法线性拟合，得到第二线性斜率k _p和第二线性截距b _p。当然，线性拟合方式还可以为其他方式，此处仅为示例，本申请不对此进行限制。 In this method, the first intermediate image under M light intensity can be used

As the Y-axis in the linear coordinate system, the image depths D ₁ , D ₂ ... D _M of the first intermediate image under the M light intensity are taken as the X-axis of the linear coordinate system, and the least squares linear fitting is performed thereon. The second linear slope k _p and the second linear intercept b _p . Of course, the linear fitting manner may also be other modes, which are merely examples here, and the present application does not limit this.

In the image processing method as described above, the pattern depth of the collected fingerprint pixel values may be directly determined, or the collected fingerprint pixel values may be processed first, and then the processed image depth of the processed image may be determined. In one implementation, the collected fingerprint pixel values may be first calibrated, and then the pattern depth of the calibrated image may be determined.

In one example, determining the pattern depth of the fingerprint pixel value in S204 as shown above may include:

And calibrating the fingerprint pixel value according to the first linear relationship to obtain a second intermediate image;

The pattern depth of the second intermediate image is determined.

For example, when a finger is pressed, the fingerprint pixel value of the nth pixel point of the fingerprint sensor output at the light intensity i can be expressed by the following formula (8).

为手指按压时在光强i下指纹传感器的第n个像素点输出的指纹像素，

为光强i下第n个像素点对应的手指反射光，

携带有较强的指纹信息，

携带有较弱指纹信息，

携带有屏幕内部各叠层的图案信息。 among them,

a fingerprint pixel outputted at the nth pixel point of the fingerprint sensor at the light intensity i when the finger is pressed,

The light reflected by the finger corresponding to the nth pixel point under the light intensity i,

Carrying strong fingerprint information,

Carrying weak fingerprint information,

Carrying pattern information for each laminate inside the screen.

The first linear relationship may include, for example, the first linear slope obtained above, and the first linear intercept.

采用下述公式(9)进行校准，得到第二中间图像。 In this embodiment, for example, according to the first linear slope k' _n and the first linear intercept b' _n , as shown in the above formula (8)

Calibration is performed using the following formula (9) to obtain a second intermediate image.

为校准后得到的第二中间图像。 among them,

The second intermediate image obtained after calibration.

It should be noted that the method for determining the depth of the pattern of the second intermediate image may be similar to the method for determining the depth of the pattern of the first intermediate image, and the details are not described herein. The pattern depth of the second intermediate image can be expressed, for example, as D _F .

On the basis of obtaining the second intermediate image, the pattern depth of the fingerprint pixel value is calibrated according to the second linear relationship in S205 of the method shown above, and obtaining the reference pixel value may include:

According to the second linear relationship, the pattern depth of the second intermediate image is calibrated to obtain a reference pixel value.

For example, the second linear relationship may include: the second linear slope k _p obtained above, and the second linear intercept b _p , and the pattern depth of the second intermediate image may be expressed as D _F . Then, in the method, according to the second linear relationship, the pattern depth of the second intermediate image is calibrated by the following formula (10) to obtain a reference pixel value.

AdjBase=D _F *k _p +b _p formula (10)

Among them, AdjBase is the reference pixel value, also called the adaptive reference pixel value.

In the image processing method of any of the above, the collected fingerprint pixel value may be directly calibrated according to the reference pixel value and the first linear relationship, or the collected fingerprint pixel value may be processed first, and then according to the The reference pixel value and the first linear relationship calibrate the processed image obtained.

In an example, the fingerprint pixel value is calibrated according to the reference pixel value and the first linear relationship in S206 as shown above, and obtaining the fingerprint image may include:

The second intermediate image is calibrated according to the reference pixel value and the first linear relationship to obtain a fingerprint image.

那么，该方法中可根据该基准像素值和该第一线性关系，采用下述公式(11)对该第二中间图像进行校准，得到指纹图像。 For example, the first linear relationship may include: the first linear slope k' _n obtained above and the first linear intercept b' _n , and the second intermediate image may be expressed as

Then, in the method, the second intermediate image is calibrated according to the reference pixel value and the first linear relationship by using the following formula (11) to obtain a fingerprint image.

Where P is the final fingerprint image.

In this embodiment, the second linear slope and the second linear intercept are determined according to the pattern depth of the first intermediate image and the first intermediate image, and the fingerprint pixel is further calibrated according to the first linear relationship to obtain the first And an intermediate image, and determining a pattern depth of the second intermediate image, and then calibrating the pattern depth of the second intermediate image according to the second linear slope and the second linear intercept to obtain a reference pixel value, where the reference pixel value includes the collected fingerprint The internal reflection light of the screen under the preset light intensity, the fingerprint image minus the reference pixel value can effectively remove the internal reflection light of the screen under the preset light intensity, so that the fingerprint image is better and clear, and the accuracy and precision of the fingerprint recognition are ensured. To improve the user experience.

The application can also provide an image processing method. This image processing method is one possible example of the image processing method described in the above embodiment. FIG. 5 is a flowchart of still another image processing method according to an embodiment of the present application. As shown in FIG. 5, the method can include:

S501. Collect the first test pixel value output by each pixel point under M light intensity when the first test unit is used for pressing.

M为大于或等于1的整数。该第一测试单元的反射率以大于绝大部分手指的反射率为佳。 The first test pixel value outputted by each of the pixel points under the M light intensity may be represented by the above formula (2), which may be:

M is an integer greater than or equal to 1. The reflectance of the first test unit is better than that of most of the fingers.

S502. Collect a second test pixel value outputted by each pixel point under M light intensity when the second test unit is pressed.

M为大于或等于1的整数。该第二测试单元的反射率以小于绝大部分手指的反射率为佳。 The second test pixel value outputted by the respective pixel points under the M light intensity may be represented by the above formula (3), which may be:

M is an integer greater than or equal to 1. The reflectance of the second test unit is preferably less than the reflectance of most of the fingers.

S503. Determine a first linear slope and a first linear intercept according to a difference between the first test pixel value and the second test pixel value of each pixel at the same light intensity m.

The difference between the first test pixel value and the second test pixel value of each pixel at the same light intensity m may be expressed as: E1, E2, ..., EM. among them,

Linear fitting of E ₁ , E ₂ , ..., E _M yields a first linear slope k' _n and a first linear intercept b' _n .

S504. Calibrate the second test pixel value outputted by each pixel point under the M light intensity according to the first linear slope and the first linear intercept to obtain a first intermediate image.

进行校准，得到M种光强下的第一中间图像

其中，

According to the first linear slope k' _n and the first linear intercept b' _n

Calibrate to obtain the first intermediate image at M light intensity

among them,

S505. Determine a pattern depth of the first intermediate image.

的图案深度，分别表示为D ₁、D ₂…D _M。 The method for determining the depth of the pattern involved in the above embodiment is sequentially determined.

The pattern depths are denoted as D ₁ , D ₂ ... D _M , respectively.

S506. Determine a second linear slope and a second linear intercept according to the pattern depths of the first intermediate image and the first intermediate image.

作为线性坐标系中的Y轴，将M种光强下的第一中间图像的图像深度D ₁、D ₂…D _M作为线性坐标系的X轴，对其进行最小二乘法线性拟合，得到第二线性斜率k _p和第二线性截距b _p。 The first intermediate image of the M light intensity can be used in the method

As the Y-axis in the linear coordinate system, the image depths D ₁ , D ₂ ... D _M of the first intermediate image under the M light intensity are taken as the X-axis of the linear coordinate system, and the least squares linear fitting is performed thereon. The second linear slope k _p and the second linear intercept b _p .

S507. Collect a fingerprint pixel value output by each pixel point under the light intensity i when the finger is pressed.

The fingerprint pixel value outputted by each pixel at the light intensity i can be expressed by the above formula (8),

S508. Calibrate the fingerprint pixel value according to the first linear slope and the first linear intercept to obtain a second intermediate image.

进行校准，得到第二中间图像

其中，

In the method, the fingerprint pixel value can be based on the first linear slope k' _n and the first linear intercept b' _n

Calibrate to get a second intermediate image

among them,

S509. Determine a pattern depth of the second intermediate image.

的图案深度可表示为D _F。 The second intermediate image

The pattern depth can be expressed as D _F .

S510. Calibrate the pattern depth of the second intermediate image according to the second linear slope and the second linear intercept to obtain a reference pixel value.

的图案深度D _F进行校准，得到基准像素值AdjBase。其中，AdjBase＝D _F*k _p+b _p。 In the method, the second intermediate image may be according to the second linear slope k _p and the second linear intercept b _p

The pattern depth D _F is calibrated to obtain a reference pixel value AdjBase. Where AdjBase=D _F *k _p +b _p .

S511. Calibrate the second intermediate image according to the reference pixel value, the first linear slope, and the first linear intercept to obtain a fingerprint image.

进行校准，得到指纹图像P，其中，

In the method, the second intermediate image may be compared according to the reference pixel value AdjBase, the first linear slope k' _n and the first linear intercept b' _n

Perform calibration to obtain a fingerprint image P, where

In the method, the second test pixel value may be calibrated to obtain a first intermediate image, and then the pattern depth of the fingerprint pixel is calibrated according to a second linear relationship between the first intermediate image and the pattern depth to obtain a reference pixel value. Then, the fingerprint pixel value is calibrated based on the reference pixel value, thereby effectively reducing the interference of the pattern information carried in the reflected light of each layer of the screen on the fingerprint image, improving the sharpness of the fingerprint image, and improving the accuracy and accuracy of the fingerprint recognition. At the same time, the method also calibrates the fingerprint pixel according to the first linear relationship between the output pixel value of each pixel and the incident light intensity, thereby effectively reducing the sensitivity difference and optical path difference of each pixel of the fingerprint sensor. Etc., effectively reducing the interference of fingerprint pixels and the optical path difference of each pixel, improving the sharpness of the fingerprint image, and improving the accuracy and accuracy of fingerprint recognition.

The following is an embodiment of the device of the present application, which can be used to implement the foregoing method embodiments of the present application, and the implementation principle and technical effects are similar.

FIG. 6 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application. The image processing apparatus can be applied to an electronic device having fingerprint recognition, the electronic device comprising: a display screen and a fingerprint sensor; the fingerprint sensor is located below the display screen, and the fingerprint sensor includes a plurality of pixel points. The image processing device can be implemented by software and/or hardware and can be integrated inside the electronic device. As shown in FIG. 6, the image processing apparatus 60 includes an acquisition module 61, a determination module 62, and a calibration module 63.

The collecting module 61 is configured to collect a fingerprint pixel value output by each pixel point of the fingerprint sensor under a preset light intensity when the finger is pressed.

a determining module 62, configured to determine a first linear relationship between an output pixel value of each pixel point of the fingerprint sensor and an incident light intensity, and a second linear relationship between the test pixel value of each pixel point and a pattern depth, And determining the pattern depth of the fingerprint pixel value.

The calibration module 63 is configured to calibrate the pattern depth of the fingerprint pixel value according to the second linear relationship to obtain a reference pixel value, and perform calibration on the fingerprint pixel value according to the reference pixel value and the first linear relationship. Fingerprint image.

In another implementation manner, the acquiring module 61 is further configured to: when the first test unit is pressed, collect the first test pixel value that is output by the each pixel at the at least one light intensity; and When the second test unit is pressed, the second test pixel value output by the pixel at the at least one light intensity is acquired.

In another implementation manner, the determining module 52 is configured to determine, according to the first test pixel value and the second test pixel value, a first between an output pixel value of each pixel point and an incident light intensity. a linear relationship; determining a pattern depth of the test pixel value, and determining a second linear relationship between the test pixel value and the pattern depth according to the test pixel value and the pattern depth of the test pixel value, wherein the test pixel value is the first A test pixel value or the second test pixel value.

In still another implementation manner, the first linear relationship includes: a first linear slope and a first linear intercept; the first linear slope is a linear slope of an output pixel value of each pixel point and an incident light intensity, The first linear intercept is a linear intercept of the output pixel value of each pixel point and the incident light intensity.

In yet another implementation manner, the reflectivity of the first test unit is greater than the reflectivity of most of the fingers; the reflectivity of the second test unit is less than the reflectivity of most of the fingers.

In a further implementation manner, the determining module 62 is configured to perform calibration on the test pixel value according to the first linear relationship to obtain a first intermediate image; and determine a pattern depth of the first intermediate image.

In a further implementation manner, the determining module 62 is configured to determine a second linear slope and a second linear intercept according to the pattern depth of the first intermediate image and the first intermediate image; wherein the second linearity The relationship includes: the second linear slope and the second linear slope, the second linear slope being a linear slope of the pattern depth of the first intermediate image and the first intermediate image, the second linear intercept being the first A linear intercept of the intermediate image and the pattern depth of the first intermediate image.

In a further implementation manner, the determining module 62 is specifically configured to determine a pattern area mask and a non-pattern area mask of the first intermediate image; and output pixel values corresponding to the pixel points corresponding to the pattern area mask and the The output pixel value of the pixel corresponding to the non-pattern area mask determines the pattern depth of the first intermediate image.

In a further implementation manner, the determining module 62 is configured to obtain a pattern depth of the first intermediate image according to a difference between an average value of the first pixel value and an average value of the second pixel value; wherein the first One pixel value is an output pixel value of a pixel corresponding to the mask of the pattern area, and the second pixel value is an output pixel value of a pixel corresponding to the mask of the non-pattern area.

In a further implementation manner, the determining module 62 is configured to: according to the preset information of the layered pattern in the display screen, the preset correspondence between the pattern information and the display position on the display screen, and the corresponding The information of the layered pattern received by the fingerprint sensor determines a display position of the layered pattern on the display screen; and according to the display position, the pattern area mask and the non-pattern area mask are determined.

In a further implementation manner, the determining module 62 is configured to determine, according to the preset information of the stacked pattern in the display screen and the reflected light inside the screen received by the fingerprint sensor when the light source is illuminated. A pattern area mask and the non-pattern area mask.

In another implementation manner, the determining module 62 is specifically configured to perform area division on the first intermediate image to obtain a plurality of sub-regions; and determine a plurality of effective sub-regions from the plurality of sub-regions; each valid sub-region includes a partial pattern area mask and a partial non-pattern area mask; determining a pattern depth of each effective sub-area according to a difference between an average value of the third pixel value and an average value of the fourth pixel value; wherein the third The pixel value is an output pixel value of a pixel corresponding to the partial pattern area mask, and the fourth pixel value is an output pixel value of the pixel corresponding to the partial non-pattern area mask; according to the pattern depth of the plurality of effective sub-areas Determining the pattern depth of the first intermediate image.

In another implementation manner, the determining module 62 is configured to perform image segmentation on the first intermediate image, remove a partial region of the first intermediate image, and obtain an effective region of the first intermediate image; The area division is performed to obtain the plurality of sub-areas.

In a further implementation manner, the determining module 62 is configured to perform calibration on the fingerprint pixel value according to the first linear relationship to obtain a second intermediate image; and determine a pattern depth of the second intermediate image.

The image processing apparatus provided in this embodiment can perform the image processing method shown in any of the above-mentioned FIG. 1 to FIG. 5, and the specific implementation and effective effects thereof can be referred to the above, and details are not described herein again.

FIG. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in FIG. 7, the electronic device 70 of the present embodiment includes a display screen 71 and a fingerprint sensor 72; the fingerprint sensor 72 is located below the display screen 71, and the fingerprint sensor 72 includes a plurality of pixel points. The electronic device 70 also includes a memory 73 and a processor 74. Display screen 71, fingerprint sensor 72, and memory 73 are coupled to processor 74 via bus 75, respectively.

The memory 73 is configured to store program instructions.

The processor 74, when used to call the program instruction stored in the memory 73, performs the following steps:

Determining a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity; determining a second linear relationship between the test pixel value and the pattern depth;

Collecting, at a finger press, a fingerprint pixel value output by each pixel at a preset light intensity;

Determining a pattern depth of the fingerprint pixel value;

According to the second linear relationship, the pattern depth of the fingerprint pixel value is calibrated to obtain a reference pixel value; and the fingerprint pixel value is calibrated according to the reference pixel value and the first linear relationship to obtain a fingerprint image.

In an implementation manner, the processor 74 is further configured to: when the first test unit is pressed, collect the first test pixel value that is output by the each pixel at the at least one light intensity; and When the second test unit performs pressing, the second test pixel value output by the pixel at the at least one light intensity is acquired.

In another implementation manner, the processor 74 is configured to determine, according to the first test pixel value and the second test pixel value, a first between an output pixel value of each pixel point and an incident light intensity. a linear relationship; determining a pattern depth of the test pixel value, and determining a second linear relationship between the test pixel value and the pattern depth according to the test pixel value and the pattern depth of the test pixel value, wherein the test pixel value is the first A test pixel value or the second test pixel value.

In still another implementation manner, the first linear relationship includes: a first linear slope and a first linear intercept.

The processor 74 is configured to determine the first linear slope and the first linear intercept according to the first test pixel value and the second test pixel value; the first linear slope is an output pixel of each pixel A linear slope of the value and incident light intensity, the first linear intercept being a linear intercept of the output pixel value of each pixel point and the incident light intensity.

In yet another implementation manner, the reflectivity of the first test unit is greater than the reflectivity of the finger; the reflectivity of the second test unit is less than the reflectivity of the finger.

In a further implementation manner, the processor 74 is configured to perform calibration on the test pixel value according to the first linear relationship to obtain a first intermediate image; and determine a pattern depth of the first intermediate image.

In a further implementation manner, the processor 74 is configured to determine a second linear slope and a second linear intercept according to the pattern depth of the first intermediate image and the first intermediate image; the second linear relationship includes The second linear slope and the second linear slope, the second linear slope being a linear slope of the pattern depth of the first intermediate image and the first intermediate image, the second linear intercept being the first intermediate image A linear intercept with the depth of the pattern of the first intermediate image.

In another implementation manner, the processor 74 is specifically configured to determine a pattern area mask and a non-pattern area mask of the first intermediate image; and output pixel values corresponding to the pixel points corresponding to the pattern area mask and the The output pixel value of the pixel corresponding to the non-pattern area mask determines the pattern depth of the first intermediate image.

In a further implementation manner, the processor 74 is configured to obtain a pattern depth of the first intermediate image according to a difference between an average value of the first pixel value and an average value of the second pixel value; wherein the first One pixel value is an output pixel value of a pixel corresponding to the mask of the pattern area, and the second pixel value is an output pixel value of a pixel corresponding to the mask of the non-pattern area.

In another implementation manner, the processor 74 is configured to: according to preset information of the layered pattern in the display screen, a preset correspondence between the pattern information and a display position on the display screen, and the corresponding The information of the layered pattern received by the fingerprint sensor determines a display position of the layered pattern on the display screen; and according to the display position, the pattern area mask and the non-pattern area mask are determined.

In another implementation manner, the processor 74 is configured to determine, according to the preset information of the stacked pattern in the display screen and the reflected light inside the screen received by the fingerprint sensor when the light source is illuminated. A pattern area mask and the non-pattern area mask.

In another implementation manner, the processor 74 is specifically configured to perform area division on the first intermediate image to obtain a plurality of sub-areas; determine a plurality of effective sub-areas from the plurality of sub-areas; each valid sub-area includes a partial pattern area mask and a partial non-pattern area mask; determining a pattern depth of each effective sub-area according to a difference between an average value of the third pixel value and an average value of the fourth pixel value; wherein the third The pixel value is an output pixel value of a pixel corresponding to the partial pattern area mask, and the fourth pixel value is an output pixel value of the pixel corresponding to the partial non-pattern area mask; according to the pattern depth of the plurality of effective sub-areas Determining the pattern depth of the first intermediate image.

In another implementation manner, the processor 74 is configured to perform image segmentation on the first intermediate image, remove a partial region of the first intermediate image, and obtain an effective region of the first intermediate image; The area division is performed to obtain the plurality of sub-areas.

In a further implementation manner, the processor 74 is configured to: calibrate the fingerprint pixel value according to the first linear relationship to obtain a second intermediate image; and determine a pattern depth of the second intermediate image.

The electronic device provided in this embodiment can perform the image processing method shown in any of the above-mentioned FIG. 1 to FIG. 5, and the specific implementation and effective effects thereof can be referred to the above, and details are not described herein again.

The embodiment of the present application further provides a computer readable storage medium, where the computer program is stored, and the computer program can be executed by the processor 74 described in FIG. 7 to implement the image processing method shown in any of the embodiments. For the realization and effective effect, refer to the above, and details are not described herein again.

A person skilled in the art can understand that all or part of the steps of implementing the above method embodiments may be completed by using hardware related to the program instructions. The foregoing program may be stored in a computer readable storage medium, and the program is executed when executed. The foregoing steps include the steps of the foregoing method embodiments; and the foregoing storage medium includes: a medium that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.

Finally, it should be noted that the above embodiments are only for explaining the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that The technical solutions described in the foregoing embodiments may be modified, or some or all of the technical features may be equivalently replaced; and the modifications or substitutions do not deviate from the technical solutions of the embodiments of the present application. range.

Claims (19)

An image processing method is applied to an electronic device having fingerprint recognition, the electronic device comprising a display screen and a fingerprint sensor located under the display screen, the fingerprint sensor comprising a plurality of pixel points, wherein Methods include: Determining a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity; Determining a second linear relationship between the test pixel value of each pixel and the depth of the pattern; When the finger is pressed, collecting the fingerprint pixel value output by the pixel at a preset light intensity; Determining a pattern depth of the fingerprint pixel value; Performing calibration on the pattern depth of the fingerprint pixel value according to the second linear relationship to obtain a reference pixel value; And correcting the fingerprint pixel value according to the reference pixel value and the first linear relationship to obtain a fingerprint image. The method according to claim 1, wherein determining a first linear relationship between an output pixel value of each pixel of the fingerprint sensor and an incident light intensity comprises: Collecting, by the first test unit, a first test pixel value outputted by the each pixel point under at least one light intensity; and, when the second test unit is pressed, acquiring the each pixel Pointing a second test pixel value outputted under the at least one light intensity; Determining, according to the first test pixel value and the second test pixel value, a first linear relationship between an output pixel value of each pixel point and an incident light intensity. The method according to claim 2, wherein the determining a second linear relationship between the test pixel value and the pattern depth of each pixel point comprises: Determining a pattern depth of the test pixel value, the test pixel value being the first test pixel value or the second test pixel value; Determining a second linear relationship between the test pixel value and the pattern depth based on the test pixel value and a pattern depth of the test pixel value. The method of claim 3 wherein said first linear relationship comprises: a first linear slope and a first linear intercept; The first linear slope is a linear slope of an output pixel value of each pixel point and an incident light intensity, and the first linear intercept is a linear cutoff of an output pixel value and an incident light intensity of each pixel point distance. The method according to claim 3, wherein the reflectance of the first test unit is greater than the reflectivity of the finger; the reflectivity of the second test unit is less than the reflectivity of the finger. The method according to any one of claims 1 to 5, wherein the determining a pattern depth of the test pixel value comprises: Performing calibration on the test pixel value according to the first linear relationship to obtain a first intermediate image; Determining a pattern depth of the first intermediate image. The method according to claim 6, wherein the determining a second linear relationship between the test pixel value and the pattern depth according to the test pixel value and the pattern depth of the test pixel value comprises: Determining a second linear slope and a second linear intercept according to the pattern depth of the first intermediate image and the first intermediate image; Wherein the second linear relationship includes: the second linear slope and the second linear intercept, the second linear slope being a linearity of a pattern depth of the first intermediate image and the first intermediate image a slope, the second linear intercept being a linear intercept of a pattern depth of the first intermediate image and the first intermediate image. The method according to claim 6, wherein the determining the depth of the pattern of the first intermediate image comprises: Determining a pattern area mask and a non-pattern area mask of the first intermediate image; Determining a pattern depth of the first intermediate image according to an output pixel value of a pixel corresponding to the pattern area mask and an output pixel value of a pixel point corresponding to the non-pattern area mask. The method according to claim 8, wherein the determining the said pixel value according to an output pixel value of a pixel corresponding to the pattern area mask and an output pixel value of a pixel point corresponding to the non-pattern area mask The pattern depth of the first intermediate image includes: Obtaining a pattern depth of the first intermediate image according to a difference between an average value of the first pixel value and an average value of the second pixel value; wherein the first pixel value is a pixel point corresponding to the pattern area mask An output pixel value, the second pixel value being an output pixel value of a pixel point corresponding to the non-pattern area mask. The method according to claim 8, wherein the determining the pattern area mask and the non-pattern area mask of the first intermediate image comprises: Corresponding relationship between the preset pattern information in the display screen, the preset relationship between the pattern information and the display position on the display screen, and the information of the stacked pattern received by the fingerprint sensor Determining a display position of the laminate pattern on the display screen; The pattern area mask and the non-pattern area mask are determined according to the display position. The method according to claim 8, wherein the determining the pattern area mask and the non-pattern area mask of the first intermediate image comprises: The pattern area mask and the non-pattern area mask are determined according to preset information of the layered pattern in the display screen and reflected light inside the screen received by the fingerprint sensor when the light source is illuminated. The method according to claim 8, wherein the output pixel value of the pixel corresponding to the pattern area mask and the output pixel value of the pixel corresponding to the non-pattern area mask are determined. The pattern depth of the first intermediate image includes: Performing area division on the first intermediate image to obtain a plurality of sub-areas; Determining a plurality of effective sub-regions from the plurality of sub-regions; each of the effective sub-regions includes: a partial pattern region mask and a partial non-pattern region mask; Determining a pattern depth of each of the effective sub-regions according to a difference between an average value of the third pixel values and an average value of the fourth pixel values; wherein the third pixel value is corresponding to the partial pattern region mask An output pixel value of the pixel, the fourth pixel value being an output pixel value of the pixel corresponding to the partial non-pattern area mask; Determining a pattern depth of the first intermediate image according to a pattern depth of the plurality of effective sub-regions. The method according to claim 12, wherein the zoning the first intermediate image to obtain a plurality of sub-regions comprises: Performing image segmentation on the first intermediate image to remove a partial region of the first intermediate image to obtain an effective region of the first intermediate image; The effective area is divided into regions to obtain the plurality of sub-areas. The method according to any one of claims 1 to 5, wherein the determining a pattern depth of the fingerprint pixel value comprises: Performing calibration on the fingerprint pixel value according to the first linear relationship to obtain a second intermediate image; Determining the pattern depth of the second intermediate image. An image processing apparatus is applied to an electronic device having fingerprint recognition, the electronic device comprising a display screen and a fingerprint sensor located under the display screen, the fingerprint sensor comprising a plurality of pixel points, wherein The device includes: An acquisition module, configured to collect a fingerprint pixel value output by each pixel of the fingerprint sensor at a preset light intensity when the finger is pressed; a determining module, configured to determine a first linear relationship between an output pixel value of each pixel point of the fingerprint sensor and an incident light intensity, and a second linear relationship between the test pixel value and the pattern depth of each pixel point And determining a pattern depth of the fingerprint pixel value; a calibration module, configured to calibrate a pattern depth of the fingerprint pixel value according to the second linear relationship to obtain a reference pixel value; and to use the fingerprint pixel value according to the reference pixel value and the first linear relationship Calibrate to get a fingerprint image. The image processing device according to claim 15, wherein The collecting module is further configured to: when the first testing unit performs pressing, collect the first test pixel value output by the each pixel at the at least one light intensity; and, press the second testing unit And acquiring, by the at least one light source, the second test pixel value outputted by the pixel. The image processing device according to claim 16, wherein The determining module is specifically configured to determine, according to the first test pixel value and the second test pixel value, a first linear relationship between an output pixel value of each pixel point and an incident light intensity; a pattern depth of the pixel value, and determining a second linear relationship between the test pixel value and the pattern depth according to the test pixel value and the pattern depth of the test pixel value, wherein the test pixel value is the first a test pixel value or the second test pixel value. An electronic device, comprising: a display screen and a fingerprint sensor located below the display screen; the fingerprint sensor includes a plurality of pixel points, wherein the electronic device further comprises: a memory and a processor The display screen, the fingerprint sensor, and the memory are respectively connected to the processor through a bus; The memory is configured to store program instructions; The processor, when used to invoke the program instructions stored in the memory, perform the image processing method according to any one of claims 1-14. A computer readable storage medium, wherein the storage medium stores a computer program, the computer program being executed by a processor to implement the image processing method according to any one of claims 1-14.

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