CN109978803B - Image processing method and device - Google Patents
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- CN109978803B CN109978803B CN201910162690.3A CN201910162690A CN109978803B CN 109978803 B CN109978803 B CN 109978803B CN 201910162690 A CN201910162690 A CN 201910162690A CN 109978803 B CN109978803 B CN 109978803B
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
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Abstract
The invention discloses an image processing method, which comprises the following steps: judging whether the acquired original fingerprint image has common-mode noise; when the original fingerprint image has common-mode noise, performing inline splicing processing on the original fingerprint image to obtain a first fingerprint image; and performing inter-line splicing processing on the first fingerprint image to obtain a second fingerprint image. The invention also provides an image processing device, which can obtain a clearer fingerprint image by performing intra-row splicing processing and inter-row splicing processing on the fingerprint image with the common mode noise, and solve the problem of fingerprint image blurring caused by the interference of the common mode noise.
Description
Technical Field
The invention relates to the technical field of fingerprint sensing systems, in particular to an image processing method and device.
Background
The fingerprint is the texture formed by the unevenness of the skin on the surface of the finger. The texture characteristic of the fingerprint has uniqueness and stability, and therefore, the fingerprint is often used as a basis for identity recognition. The fingerprint sensing system is a sensing system for identifying identity through fingerprints and comprises a capacitance type fingerprint sensing system. However, when the capacitive fingerprint sensing system is used for fingerprint image recognition, the capacitive fingerprint sensing system is very susceptible to interference of common mode noise (such as charger noise, transformer noise and switching power supply noise), which may cause fingerprint image recognition abnormality. And the randomness of the common mode noise makes the image recovery difficult and can not be recovered by a normal filtering method.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides an image processing method and apparatus, which can determine whether a fingerprint image acquired by a fingerprint sensing system is interfered by common mode noise, and can solve the problem of fingerprint image blur caused by common mode noise.
An image processing method according to the present invention is characterized by comprising: judging whether the acquired original fingerprint image has common-mode noise; when the original fingerprint image has common-mode noise, performing inline splicing processing on the original fingerprint image to obtain a first fingerprint image; and performing inter-line splicing processing on the first fingerprint image to obtain a second fingerprint image.
Preferably, the intra-row splicing process includes: performing intra-row grouping on each row of the original fingerprint image; acquiring the edge slope of each group in the same line; longitudinally stretching one of the two adjacent groups according to the edge slope of the same row; and translating one group according to the edge height difference of two adjacent groups in the same row.
Preferably, the longitudinal stretching is such that the slope of the edges of two adjacent packets of the same row is the same.
Preferably, the translation is such that the difference in edge height between two adjacent subgroups in the same row is zero.
Preferably, the inter-row splicing process includes: acquiring a mean value and a standard deviation of each line in the first fingerprint image; carrying out standard deviation normalization processing according to the mean value and the standard deviation of each line to obtain a third fingerprint image; and carrying out mean value normalization processing on the third fingerprint image.
Preferably, the standard deviation normalization process includes: taking one of the rows as a reference row, and acquiring the ratio of the standard deviation of the other rows to the standard deviation of the reference row; and acquiring a difference value between each line of the first fingerprint image and the corresponding line mean value, and multiplying the difference value by the ratio to obtain a third fingerprint image.
Preferably, the mean normalization process includes: adding each line of the third fingerprint image to the mean value of the reference lines to obtain a second fingerprint image.
Preferably, the judging whether the acquired original fingerprint image has common-mode noise includes: performing intra-row grouping on each row of the original fingerprint image; acquiring the edge slope of each group in the same line; acquiring the edge height difference of two adjacent groups according to the edge slope of each group; summing all the edge height differences to obtain an offset distance; when the offset distance is greater than a preset threshold value, the original fingerprint image has common-mode noise.
Preferably, the method further comprises the following steps: and performing Gaussian filtering on the second fingerprint image.
Preferably, the original fingerprint image is acquired in a line-by-line scanning manner.
Preferably, the common mode noise includes charger noise, transformer noise, and switching power supply noise.
An image processing apparatus according to the present invention is characterized by comprising: the judging module is used for judging whether the acquired original fingerprint image has common-mode noise; the first processing module is used for performing inline splicing processing on the original fingerprint image to obtain a first fingerprint image when the original fingerprint image has common-mode noise; and the second processing module is used for performing inter-line splicing processing on the first fingerprint image to obtain a second fingerprint image.
Preferably, the first processing module comprises: the grouping unit is used for carrying out intra-row grouping on each row of the original fingerprint image; the edge slope unit is used for acquiring the edge slope of each group in the same line; the stretching unit is used for longitudinally stretching one of the two groups according to the edge slope of the two adjacent groups in the same row; and the translation unit is used for translating one group according to the edge height difference of two adjacent groups in the same row.
Preferably, the longitudinal stretching is such that the slope of the edges of two adjacent packets of the same row is the same.
Preferably, the translation is such that the difference in edge height between two adjacent subgroups in the same row is zero.
Preferably, the second processing module comprises: the acquisition unit is used for acquiring the mean value and the standard deviation of each line in the first fingerprint image; the first normalization unit is used for carrying out standard deviation normalization processing according to the mean value and the standard deviation of each line to obtain a third fingerprint image; and the second normalization unit is used for carrying out mean value normalization processing on the third fingerprint image.
Preferably, the first normalization unit is configured to use one of the rows as a reference row, and obtain a ratio between a standard deviation of the other row and a standard deviation of the reference row; and acquiring a difference value between each line of the first fingerprint image and the corresponding line mean value, and multiplying the difference value by the ratio to obtain a third fingerprint image.
Preferably, the second normalization unit is configured to add each line of the third fingerprint image to the mean of the reference lines to obtain the second fingerprint image.
Preferably, the judging module is configured to perform intra-row grouping on each row of the original fingerprint image; acquiring the edge slope of each group in the same line; acquiring the edge height difference of two adjacent groups according to the edge slope of each group; summing all the edge height differences to obtain an offset distance; when the offset distance is greater than a preset threshold value, the original fingerprint image has common-mode noise.
Preferably, the method further comprises the following steps: and the filtering module is used for carrying out Gaussian filtering on the second fingerprint image.
Preferably, the original fingerprint image is acquired in a line-by-line scanning manner.
Preferably, the common mode noise includes charger noise, transformer noise, and switching power supply noise.
The invention has the beneficial effects that: the invention discloses an image processing method and device, which can obtain a clearer fingerprint image by carrying out noise detection on a fingerprint image acquired by a fingerprint sensor and carrying out intra-row splicing processing and inter-row splicing processing on the fingerprint image with common-mode noise, and solve the problem of fingerprint image blurring caused by the interference of the common-mode noise.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is a flow chart illustrating an image processing method provided by an embodiment of the invention;
FIG. 2 shows a flow chart of step S100 in an embodiment of the invention;
3 a-3 c show waveform diagrams of fingerprint images of an embodiment of the invention;
FIG. 4 shows a flowchart of step S200 in an embodiment of the present invention;
FIG. 5 shows a flowchart of step S300 in an embodiment of the invention;
fig. 6 is a block diagram showing a configuration of an image processing apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram illustrating a first processing module in the image processing apparatus according to the embodiment of the present invention;
fig. 8 is a schematic structural diagram illustrating a second processing module in the image processing apparatus according to the embodiment of the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, 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 belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 shows a flowchart of an image processing method provided by an embodiment of the present invention. As shown in fig. 1, the image processing method provided by the present invention includes the following steps.
In step S100, it is determined whether the acquired original fingerprint image has common mode noise.
In this embodiment, when a fingerprint sensing system and other related devices are used to perform corresponding operations such as fingerprint entry or unlocking, a failure of fingerprint entry or unlocking sometimes occurs, and one of the important reasons for the failure is that a fingerprint image acquired by the fingerprint sensing system may be interfered by external common mode noise (such as charger noise, transformer noise, and switching power supply noise).
In the embodiment of the invention, the noise detection step is carried out after the fingerprint sensor collects the original fingerprint image and the input/unlock failure of the collected original fingerprint image is carried out, the noise detection is carried out on the original fingerprint image, and whether the collected original fingerprint image has common-mode noise or not is judged.
As shown in fig. 2, step S100 includes steps S110 to S150.
In step S110, each line of the original fingerprint image is intra-line grouped.
In the embodiment of the invention, the mode of acquiring the original fingerprint image by the capacitive fingerprint sensing system is line-by-line scanning, and each line is acquired for multiple times in the line-by-line scanning process, and multiple pixels can be acquired each time. Taking a row acquisition 5 times, each acquisition 8 pixels for example, we can divide each row of the original fingerprint image into 5 groups, each group comprising 8 pixels.
Since a plurality of pixels in one collected group have the same common-mode noise N1; and the collected pixels of the next group have common-mode noise of N2, and so on, the common-mode noise between two adjacent groups in each row is different.
For a fingerprint image acquired by a fingerprint sensing system, in the same row of the acquired fingerprint image, because the original trend is maintained between a plurality of adjacent pixels, a certain offset exists between two adjacent groups of pixels, and an offset value d needs to be estimated and recorded.
Step S120: the edge slope of each packet in the same row is obtained.
In this embodiment, when the captured fingerprint image has common mode noise, the original fingerprint image changes from X to k X + d. Where k is the slope of the fingerprint image, d is the offset value (or edge height difference), both of which are arbitrary real numbers.
Fig. 3a shows a waveform diagram of an original fingerprint image. Wherein, for the first row, the edge slope k11 of the first packet and the edge slope k12 of the second packet are calculated at the edges where the first packet and the second packet are adjacent. And in the same way, the edge slopes k 11-k 1n of each group in the first line in the original fingerprint image are obtained.
And in the same way, obtaining the edge slope km 1-kmn of each group in the m-th line in the original fingerprint image.
Step S130: and acquiring the edge height difference of two adjacent groups according to the edge slope of each group.
Predicting the position of a pixel adjacent to one packet in the next packet according to the edge slope of the packet; and then recording the actual position of the pixel point in the actually acquired fingerprint image, and calculating the height difference between the predicted position and the actual position as the edge height difference.
In fig. 3a, for the first row, the position where the first pixel in the next grouping is likely to appear is predicted according to the edge slope k11 of the first grouping, and then the height difference between the two positions is obtained as the edge height difference d11 according to the actual position of the first pixel in the next grouping. By analogy, the edge height difference d11-d1(n-1) between two adjacent packets in the first row in the original fingerprint image is obtained.
By analogy, the edge height difference dm1-dm (n-1) between two adjacent packets in the mth row in the original fingerprint image is obtained.
Step S140: summing all edge height differences yields the offset distance.
In the step, the edge height differences of all the adjacent two groups in each group of each row are obtained, and the sum of all the edge height differences in each row is calculated, wherein the sum of the edge height differences d1 in the first row is d11+ d12+ … … + d1(n-1), the sum of the edge height differences in the second row is d2 in d21+ d22+ … … + d2(n-1), and the sum of the edge height differences in the m-th row is dm1+ dm2+ … … + dm (n-1). The edge height differences of adjacent groups in all rows are then summed to obtain the offset distance d of the original fingerprint image, d being d1+ d2+ … … + dm.
Step S150: when the offset distance is greater than a preset threshold, the original fingerprint image has common-mode noise.
In this step, the obtained offset distance d is compared with a preset threshold dx, and whether the acquired fingerprint image is interfered by common mode noise is judged. Specifically, when the offset distance d is greater than a preset threshold dx, it is indicated that the acquired fingerprint image is interfered by common-mode noise; when the offset distance d is less than or equal to the preset threshold dx, it indicates that the acquired fingerprint image is not interfered by common-mode noise.
If the fingerprint image is detected to have no common-mode noise, the cause of the common-mode noise interference can be eliminated to facilitate further problem detection, which is not described herein.
In step S200, when the original fingerprint image has common mode noise, the original fingerprint image is subjected to inline stitching processing to obtain a first fingerprint image.
In the embodiment of the invention, the intra-row signal splicing method of the fingerprint image with noise interference is mainly applied to the condition that the general trend of signals subjected to common-mode noise interference is the same as that of original signals in a plurality of adjacent pixels of the fingerprint image acquired each time.
As shown in fig. 4, step S200 includes steps S210 to S250.
In step S210, each line of the original fingerprint image having common mode noise is intra-line grouped.
In step S220, the edge slope of each packet in the same line is acquired.
Fig. 3a shows a waveform diagram of an original fingerprint image. Wherein, for the first row, the edge slope k11 of the first packet and the edge slope k12 of the second packet are calculated at the edges where the first packet and the second packet are adjacent. And in the same way, the edge slopes k 11-k 1n of each group in the first line in the original fingerprint image are obtained.
And in the same way, obtaining the edge slope km 1-kmn of each group in the m-th line in the original fingerprint image.
In step S230, one of the two adjacent subgroups in the same row is longitudinally stretched according to the edge slope of the two subgroups.
In this embodiment, the longitudinal stretching makes the slope of the edge of two adjacent groups in the same row the same.
Figure 3b shows a waveform of the original fingerprint image after longitudinal stretching. For the first row, the second packet is longitudinally stretched with respect to the edge slope k11 of the first packet such that the edge slopes of the first and second packets are the same.
And step S240, translating one group according to the edge height difference of two adjacent groups in the same row.
In this embodiment, the edge height difference between two adjacent packets is obtained according to the edge slope of each packet, and then one packet is translated according to the edge height difference between two adjacent packets in the same row, so that the edge height difference between two adjacent packets in the same row is zero.
As shown in fig. 3c, for the first row, the second packet is translated longitudinally such that the edge height difference between the first packet and the second packet is zero.
In step S300, the first fingerprint image is subjected to inter-line stitching processing to obtain a second fingerprint image.
In this embodiment, the inter-line signal splicing method for a fingerprint image with noise interference is mainly applied to the case that noise between lines is random and has no similar slope relationship.
As shown in fig. 5, step S300 includes steps S310 to S330.
In step S310, the mean and standard deviation of each line in the first fingerprint image are acquired.
In step S320, a standard deviation normalization process is performed according to the mean and the standard deviation of each line to obtain a third fingerprint image.
In this embodiment, one of the rows is used as a reference row, and a ratio between a standard deviation of the other rows and a standard deviation of the reference row is obtained; and acquiring a difference value between each line of the first fingerprint image and the corresponding line mean value, and multiplying the difference value by the ratio to obtain a third fingerprint image. The reference row may be the first row or may be any row.
And step S330, carrying out mean value normalization processing on the third fingerprint image.
In this embodiment, each line of the third fingerprint image is added to the mean of the reference lines to obtain the second fingerprint image.
In a preferred embodiment, the image processing method further includes the step S400
In step S400, gaussian filtering is performed on the second fingerprint image.
In this embodiment, since interferences such as burrs and cracks may be generated during the intra-line stitching process and the inter-line stitching process, the second fingerprint image needs to be filtered.
The embodiment of the invention also discloses an image processing device. Please refer to fig. 6, 7 and 8 for understanding.
Fig. 6 is a block diagram showing a configuration of an image processing apparatus according to an embodiment of the present invention.
As shown in fig. 6, in the present embodiment, the image processing apparatus includes a fingerprint sensor 100, a determination module 200, a first processing module 300, a second processing module 400, and a filtering module 500, which are connected in sequence.
In this embodiment, the fingerprint sensor 100 is configured to collect an original fingerprint image and send the collected original fingerprint image to the determining module 200.
Preferably, the fingerprint sensor 100 acquires the original fingerprint image in a line-by-line scanning manner.
In this embodiment, the determining module 200 is configured to receive an original fingerprint image collected by the fingerprint sensor 100, detect the original fingerprint image, and determine whether the original fingerprint image has common mode noise.
The judgment module 200 is used for performing intra-row grouping on each row of the original fingerprint image acquired by the fingerprint sensor 100; acquiring the edge slope of each group in the same line; acquiring the edge height difference of two adjacent groups according to the edge slope of each group; summing all the edge height differences to obtain an offset distance; and when the offset distance is larger than a preset threshold value, judging that the original fingerprint image has common-mode noise.
In this embodiment, the first processing module 300 is configured to receive the determination result and the original fingerprint image sent by the determining module 200, and perform inline stitching on the original fingerprint image to obtain a first fingerprint image when the original fingerprint image has common-mode noise.
The first processing module 300 mainly works in a plurality of adjacent pixels of the fingerprint image acquired each time, and the general trend of the signal subjected to the common mode noise interference is the same as that of the original signal.
In this embodiment, the second processing module 400 is configured to receive the first fingerprint image sent by the first processing module 300, and perform inter-line stitching processing on the first fingerprint image to obtain a second fingerprint image.
The second processing module 400 is mainly applied to noise randomization between rows and works when there is no similar slope relationship between each other.
The filtering module 500 is configured to receive the second fingerprint image sent by the second processing module 400, perform gaussian filtering on the second fingerprint image, and filter interferences such as burrs and fractures generated during intra-row splicing processing and inter-row splicing processing.
Fig. 7 is a schematic structural diagram illustrating a first processing module in the image processing apparatus according to the embodiment of the present invention.
As shown in fig. 7, in the present embodiment, the first processing module 300 includes a grouping unit 310, an edge slope unit 320, a stretching unit 330, and a translating unit 340, which are connected in sequence.
In this embodiment, the grouping unit 310 is configured to receive an original fingerprint image and perform intra-row grouping on each row of pixel points of the original fingerprint image.
Taking the example of the fingerprint sensor 100 acquiring 5 times a line while scanning line by line, each acquiring 8 pixels, the grouping unit 310 may divide each line of the original fingerprint image into 5 groups, each group including 8 pixels.
In this embodiment, the edge slope unit 320 is configured to obtain the edge slope of each packet in the same row.
For the first row, the edge slope k11 of the first packet and the edge slope k12 of the second packet are calculated at the edges where the first packet and the second packet are adjacent. And in the same way, the edge slopes k 11-k 1n of each group in the first line in the original fingerprint image are obtained.
And in the same way, obtaining the edge slope km 1-kmn of each group in the m-th line in the original fingerprint image.
In this embodiment, the stretching unit 330 is configured to stretch one of the groups longitudinally according to the slope of the edge of two adjacent groups in the same row, so that the slope of the edge of two adjacent groups in the same row is the same.
In this embodiment, the shifting unit 340 is configured to shift one packet according to the edge height difference between two adjacent packets in the same row, so that the edge height difference between two adjacent packets in the same row is zero.
Fig. 8 is a schematic structural diagram illustrating a second processing module in the image processing apparatus according to the embodiment of the present invention.
As shown in fig. 8, in this embodiment, the second processing module 400 includes an obtaining unit 410, a first normalizing unit 420, and a second normalizing unit 430, which are connected in sequence.
In this embodiment, the obtaining unit 410 is configured to receive the first fingerprint image output by the first processing module 300, and obtain a mean value and a standard deviation of each row of pixel points in the first fingerprint image.
In this embodiment, the first normalization unit 420 is configured to perform a standard deviation normalization process according to the mean and the standard deviation of each line to obtain a third fingerprint image.
Specifically, taking one of the rows as a reference row, and acquiring a ratio between the standard deviation of the other rows and the standard deviation of the reference row; and acquiring a difference value between each line of the first fingerprint image and the corresponding line mean value, and multiplying the difference value by the ratio to obtain a third fingerprint image. The reference row may be the first row or may be any row.
Preferably, the first row or the middle row is selected as the reference row in this embodiment.
In this embodiment, the second normalization unit 430 is configured to perform mean normalization on the third fingerprint image.
In particular, the second normalization unit 430 is configured to add each line of the third fingerprint image to the mean of the reference lines to obtain the second fingerprint image.
In the embodiment of the invention, the fingerprint image collected by the fingerprint sensor is subjected to noise detection, and the fingerprint image with common-mode noise is subjected to in-line splicing processing and inter-line splicing processing, so that a clearer fingerprint image can be obtained, the problem of fingerprint image blurring caused by the interference of the common-mode noise is solved, and the interference of the common-mode noise (such as charger noise, transformer noise and switching power supply noise) on the image received by the capacitive fingerprint sensing system is reduced to a certain extent.
It should be noted that, in this document, the contained terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: it should be understood that the above examples are only for clearly illustrating the present invention and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the scope of the invention.
Claims (18)
1. An image processing method, comprising:
judging whether the acquired original fingerprint image has common-mode noise;
when the original fingerprint image has common-mode noise, performing inline grouping on the original fingerprint image with the common-mode noise, and performing inline splicing processing on two adjacent groups in the same line to obtain a first fingerprint image;
acquiring the mean value and the standard deviation of each line in the first fingerprint image, performing inter-line splicing processing on the first fingerprint image based on the mean value and the standard deviation to obtain a second fingerprint image,
wherein the inline tiling process comprises:
acquiring the edge slope of each group in the same line;
longitudinally stretching one of the two adjacent groups according to the edge slope of the same row;
translating one group according to the edge height difference of two adjacent groups in the same row; and
the inter-row splicing processing comprises the following steps:
carrying out standard deviation normalization processing according to the mean value and the standard deviation of each line to obtain a third fingerprint image;
and carrying out mean value normalization processing on the third fingerprint image.
2. The image processing method according to claim 1, wherein the longitudinal stretching is such that the edge slopes of two adjacent packets in the same row are the same.
3. The image processing method according to claim 1, wherein the shifting is performed such that the edge height difference between two adjacent packets in the same row is zero.
4. The image processing method according to claim 1, wherein the standard deviation normalization process includes:
taking one of the rows as a reference row, and acquiring the ratio of the standard deviation of the other rows to the standard deviation of the reference row;
and acquiring a difference value between each line of the first fingerprint image and the corresponding line mean value, and multiplying the difference value by the ratio to obtain a third fingerprint image.
5. The image processing method according to claim 4, wherein the mean normalization process includes:
adding each line of the third fingerprint image to the mean value of the reference lines to obtain a second fingerprint image.
6. The image processing method of claim 1, wherein the determining whether the captured original fingerprint image has common-mode noise comprises:
performing intra-row grouping on each row of the original fingerprint image;
acquiring the edge slope of each group in the same line;
acquiring the edge height difference of two adjacent groups according to the edge slope of each group;
summing all the edge height differences to obtain an offset distance;
when the offset distance is greater than a preset threshold value, the original fingerprint image has common-mode noise.
7. The image processing method according to claim 1, further comprising:
and performing Gaussian filtering on the second fingerprint image.
8. The image processing method according to any one of claims 1 to 7, wherein the original fingerprint image is acquired in a line-by-line scanning manner.
9. The image processing method according to any one of claims 1 to 7, wherein the common mode noise includes charger noise, transformer noise, and switching power supply noise.
10. An image processing apparatus characterized by comprising:
the judging module is used for judging whether the acquired original fingerprint image has common-mode noise;
the first processing module is used for performing inline grouping on the original fingerprint image with the common-mode noise when the original fingerprint image has the common-mode noise, and performing inline splicing processing on two adjacent groups in the same row to obtain a first fingerprint image;
a second processing module, configured to obtain a mean value and a standard deviation of each line in the first fingerprint image, and perform inter-line stitching processing on the first fingerprint image based on the mean value and the standard deviation to obtain a second fingerprint image,
wherein the first processing module comprises:
the grouping unit is used for carrying out intra-row grouping on each row of the original fingerprint image;
the edge slope unit is used for acquiring the edge slope of each group in the same line;
the stretching unit is used for longitudinally stretching one of the two groups according to the edge slope of the two adjacent groups in the same row;
the translation unit is used for translating one group according to the edge height difference of two adjacent groups in the same row;
the second processing module comprises:
the acquisition unit is used for acquiring the mean value and the standard deviation of each line in the first fingerprint image;
the first normalization unit is used for carrying out standard deviation normalization processing according to the mean value and the standard deviation of each line to obtain a third fingerprint image;
and the second normalization unit is used for carrying out mean value normalization processing on the third fingerprint image.
11. The image processing apparatus according to claim 10, wherein the longitudinal stretching is such that edge slopes of two adjacent packets in the same row are the same.
12. The image processing apparatus according to claim 10, wherein the translation is such that the edge height difference between two adjacent packets in the same row is zero.
13. The image processing apparatus according to claim 10, wherein the first normalization unit is configured to obtain, with one of the rows as a reference row, a ratio between a standard deviation of the other row and a standard deviation of the reference row; and acquiring a difference value between each line of the first fingerprint image and the corresponding line mean value, and multiplying the difference value by the ratio to obtain a third fingerprint image.
14. The image processing apparatus according to claim 13, wherein the second normalization unit is configured to add each line of the third fingerprint image to the average of the reference lines to obtain the second fingerprint image.
15. The image processing apparatus according to claim 10, wherein the determining module is configured to perform intra-grouping on each line of the original fingerprint image; acquiring the edge slope of each group in the same line; acquiring the edge height difference of two adjacent groups according to the edge slope of each group; summing all the edge height differences to obtain an offset distance; when the offset distance is greater than a preset threshold value, the original fingerprint image has common-mode noise.
16. The image processing apparatus according to claim 10, further comprising:
and the filtering module is used for carrying out Gaussian filtering on the second fingerprint image.
17. An image processing apparatus according to any one of claims 10 to 16, wherein the original fingerprint image is acquired in a line-by-line scanning manner.
18. The image processing apparatus according to any one of claims 10 to 16, wherein the common mode noise includes charger noise, transformer noise, and switching power supply noise.
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| CN107590475A (en) * | 2017-09-22 | 2018-01-16 | 北京小米移动软件有限公司 | The method and apparatus of fingerprint recognition |
| CN108323206A (en) * | 2018-02-06 | 2018-07-24 | 深圳市汇顶科技股份有限公司 | Fingerprint data processing method and device, computer-readable storage medium |
| CN108596060A (en) * | 2018-04-12 | 2018-09-28 | 上海思立微电子科技有限公司 | Fingerprint image processing method, fingerprint identification device and electronic equipment |
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| US9519819B2 (en) * | 2014-07-14 | 2016-12-13 | Fingerprint Cards Ab | Method and electronic device for noise mitigation |
| US9639733B2 (en) * | 2014-11-25 | 2017-05-02 | Cypress Semiconductor Corporation | Methods and sensors for multiphase scanning in the fingerprint and touch applications |
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| CN107590475A (en) * | 2017-09-22 | 2018-01-16 | 北京小米移动软件有限公司 | The method and apparatus of fingerprint recognition |
| CN108323206A (en) * | 2018-02-06 | 2018-07-24 | 深圳市汇顶科技股份有限公司 | Fingerprint data processing method and device, computer-readable storage medium |
| CN108596060A (en) * | 2018-04-12 | 2018-09-28 | 上海思立微电子科技有限公司 | Fingerprint image processing method, fingerprint identification device and electronic equipment |
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