JP2001285641A - Image processing method, image processing apparatus and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing method, an image processing apparatus and a recording medium that can suppress noise and emphasize sharpeness in a digital image without causing an unpleasant unevenness due to the noise and an unnatural artifact or the like. SOLUTION: Sharpness emphasis, smoothing and edge detection are applied to original image data, edge and noise intermingled image data of an object are obtained from the obtained sharpness emphasis image data and smoothed image data, a noise weighting coefficient is obtained from edge emphasis data obtained by the edge detection, noise data are obtained from the weighting coefficient and the intermingled image data, a noise suppression distribution function denoting the spread of noise suppression is set, convolution integral is applied to this function and the noise data to calculate the noise suppression distribution, a noise suppression component is obtained by multiplying the noise data with the noise suppression distribution and the component is magnified and subtracted from the sharpness emphasis image data to obtain processed image data so as to solve the tasks above.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a digital image processing method, apparatus, and recording medium for suppressing noise components such as granularity of a digital image and enhancing sharpness.

[0002]

2. Description of the Related Art In a system in which a digital image obtained by scanning an image of a silver halide photograph by an image input scanner or a digital image photographed by a digital still camera or the like is processed and output by an image output printer, an output method is used. The sharpened image is greatly deteriorated by a scanner, a camera and a printer, and in order to recover the sharpness, sharpness enhancement has conventionally been performed by a Laplacian filter or an unsharp mask (USM). However, since the sharpness of the image is improved and the noise (noise) such as graininess is adversely affected, in an image having noise such as graininess, only a modest sharpness enhancement can be performed within a range where the graininess is allowed. It has been difficult to improve the image quality more than the original image.

Some digital image processing methods have been proposed as image processing methods for removing graininess as noise and enhancing sharpness. However, since an averaging or blurring method is used as a method for removing graininess, blurring occurs. There is a problem that the granular pattern is visually uncomfortable or a minute subject structure is unnaturally erased, and is not suitable for an aesthetic image such as a photograph.

[0004] Degradation of sharpness due to an optical system such as a camera in images of photographs, prints, televisions, various copying machines, etc.
Noise is suppressed to recover the granularity and sharpness deterioration inherent in photographic photosensitive materials, or noise (noise) and sharpness deterioration added when digitizing original images such as photographs and prints with an image input device. And various methods have been devised as image processing methods for enhancing sharpness. For example,
In the conventional image processing method, a method called smoothing or coring is used as a grain removal processing method, and an unsharp mask (USM; Unsh
arp Masking), Laplacian, or high-pass filter processing. However, with these conventional grain removal processing methods, when graininess is suppressed, unnatural artifacts occur, or undesirable microstructures of the image that should not be suppressed are suppressed together with the graininess. Had disadvantages.

For example, JP-T-57-500131 and JP-A-57-500354 and "Digital Enhancement Method of Unsharp, Granular, and Conspicuous Photographic Image", International Conference on Electronic Image Processing, July 1982, No. 179. ~ 183
Page (PG Powell and BEBayer, "A Method for the D
igital Enhancement of Unsharp, Grainy Photographic
Images '', Proceedingus of the International Confer
ence on Electronic Image Processing, Jul. 26-28,198
2, pp. 179-183), a processing method using a smoothing processing method (low-pass filter) as a graininess suppression method and an unsharp mask (high-pass filter) as a sharpness enhancement method. Is used. The smoothing process is performed by applying Gauss to the signal value of n × n pixels
This is a process of suppressing granularity by smoothing the signal by multiplying by a sian-type weight or the like. In the sharpness enhancement processing, first, a differential value in the direction from the center pixel to the surrounding pixels is obtained using an image signal of m × m pixels, and if the value is smaller than a set threshold, it is regarded as granular or noise and coring is performed. Removed by processing, take the sum of the differential value larger than the remaining threshold,
Sharpness enhancement is performed by multiplying the smoothed signal by a constant of 1.0 or more.

In this processing method, since the granular pattern is blurred, the density contrast of the granular pattern is reduced, but a large uneven pattern made up of large settlements (granular mottle) of the particles constituting the granular pattern is visually noticeable. And has the disadvantage of appearing as unpleasant granules. Further, since the image is discriminated from the image with the set threshold value (coring processing), an image signal having a low contrast is misidentified as an image signal, and is suppressed or removed together with the image particle. Has the disadvantage that discontinuities occur at the boundary of the image and unnatural artifacts are seen in the image. In particular, this drawback appears in fine images such as lawns and carpets, and images in which textures such as cloth are depicted, resulting in visually very unnatural and undesirable artifacts.

[0007]

By the way, in the above-mentioned conventional grain suppression / sharpness-enhanced image processing method, sharpness is enhanced by an unsharp mask, and graininess is blurred or reduced by smoothing. By separating the granular signal (noise) signal from the contour signal at the signal level, sharpening the contour signal and suppressing the graininess in the smooth region, the small signal is regarded as grainy and processed. However, there is a problem that image detail signals similar to the above, that is, image signals of clothing texture, hair, etc. are suppressed together with graininess, and there is a disadvantage that an image is visually uncomfortable as an image processing artifact. That is, in such a conventional method, an image is blurred using averaging as a method of suppressing graininess, and a blurred grain pattern (“blur grain”) becomes small as fluctuation of density in an image and graininess is improved. It looks like it's gone,
Conversely, a granular pattern that has a small density fluctuation but is blurred and spread is visually recognized as an unpleasant pattern, and is particularly noticeable in a uniform subject such as a face or skin such as a portrait photograph, or a wall or the sky. There was a problem.

In the conventional method of separating a granular (noise, noise) region and a contour region from an original image at a signal level, a contour region and a flat region are identified from a difference signal between the original image and the blurred image, and each region is identified. By using different coefficients such as unsharp mask and Laplacian to reduce graininess in flat areas, sharpness is enhanced in contour areas and graininess is suppressed without blurring edges in contour areas. In addition, since the recognition and separation of the granular region are uniformly performed at a signal level that is a threshold, there is a problem that discontinuity occurs at the boundary. Further, in such a conventional method, an unsharp mask or a Laplacian is used as an edge emphasis or sharpness emphasis method. There was a problem of giving a natural impression. Problems relating to suppression of noise such as graininess and sharpness enhancement are not problems unique to silver halide photography, but also include shot noise and electric noise when taking an image with a digital still camera or the like. It occurs as a problem of suppressing various noises and enhancing sharpness.

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned state of the art, and it has been proposed that blurs caused by a camera, photographic light-sensitive images, images of silver halide photographs, digital still camera images, printing, television, various copying machines, and the like can be obtained. Noise (noise) and sharpness degradation inherent in the original image such as graininess or blur of the material, or noise added when the original image is digitized by the image input device, or shot noise when photographing with a digital still camera When performing processing for recovering the sharpness degradation, the problem of the above-described conventional technology, that is, the problem that noise is suppressed by performing smoothing and large unevenness appears visually uncomfortable, The problem that low image signals are recognized as grainy or noise and are suppressed or removed, and the boundary between the noise removal area and the sharpness enhancement area is discontinuous An image processing method for a digital image that suppresses noise and enhances sharpness of an image without causing a problem that an unnatural artifact is seen in an image, an image processing apparatus that performs the method, and an image processing method that performs the method It is an object of the present invention to provide a computer-readable recording medium.

[0010]

In order to achieve the above-mentioned object, the present invention provides a sharpness-enhanced image data in which original image data is subjected to sharpness enhancement processing and an image is sharpened with noise contained in the image. Then, a smoothing process is performed on the original image data to create smoothed image data, and the smoothed image data is subtracted from the sharpness-enhanced image data to perform sharpness-enhanced edge and sharpness-enhanced subject image. Mixed image data in which mixed noise is mixed, edge detection is performed from the original image data to obtain edge intensity data for identifying a subject edge region and a noise region, and the degree of the noise region is determined from the edge intensity data. A weighting coefficient for the noise region shown is obtained, and the mixed image data is multiplied by the weighting coefficient for the noise region. Noise data, a noise suppression distribution function indicating the extent of noise suppression in the original image data is set, and convolution integration of the noise data and the noise suppression distribution function is performed to obtain a noise suppression distribution. The noise suppression component image data is calculated by multiplying the data by the noise suppression distribution, and the noise suppression component image data is scaled and subtracted from the sharpness-enhanced image data to reduce noise in the noise region of the original image. An object of the present invention is to provide an image processing method for suppressing noise in a digital image, which is characterized in that an image in which components are selectively suppressed is created.

Here, the noise suppression distribution function is a monotonically decreasing function in which the value is maximum at the center position of the noise suppression spread in the pixels of the original image, and the value decreases as the distance from the center position increases. Preferably, or preferably, the noise suppression distribution function is a rectangular function having a constant value within a range in which noise suppression is performed on pixels of the original image.

Preferably, the range of the noise suppression distribution function that exerts the noise suppression on the pixels of the original image is an area in the range of 1 to 15 in terms of the number of pixels. It is preferable to set the value by determining the position of the boundary of the range where the noise is suppressed in the pixels of the original image and the value at this position.

In the noise suppression distribution function, when r is the distance from the center position of the noise suppression spread in the pixels of the original image, and a is the suppression range constant that determines the range of the noise suppression spread in the pixels of the original image, It is preferably a function s (r) represented by the following equation (1): s (r) = exp (−r / a) (1) s (r) = exp (−r ² / a ² ) ( 2) s (r) = rect (r / a) (3) Here, “rect (r / a)” in Expression (3) is a rectangular function having a value of “1”.

Further, in order to achieve the above object, the present invention provides a sharpness enhancement process for performing sharpness enhancement processing on original image data and creating sharpness enhanced image data in which noise included in the image is sharpened together with the image. And a smoothing processing unit that performs smoothing processing on the original image data to create smoothed image data, and subtracts the smoothed image data created by the smoothing processing unit from the sharpness enhanced image data. An edge / noise mixed component extraction unit that creates mixed image data in which the edges of the sharpness-enhanced subject image and the sharpness-enhanced noise are mixed; and performs edge detection from the original image data to obtain a subject edge region and noise. An edge detecting section for obtaining edge intensity data for identifying a region, and an edge detecting section for obtaining the edge intensity data. A noise region weighting coefficient calculation unit for obtaining a noise region weighting coefficient indicating the degree of the noise region from the intensity data, and the mixed image data created by the edge / noise mixed component extraction unit, the noise region weighting coefficient calculation unit Multiplying the obtained noise area weighting coefficient, noise component identification and separation unit to obtain noise data,
A noise suppression distribution function setting unit that sets a noise suppression distribution function that represents the extent of noise suppression in the original image data, and the noise data that is determined by the noise component identification and separation unit and the noise suppression distribution function that is set by the noise suppression distribution function setting unit. A noise suppression distribution calculation unit for performing convolution integration with a noise suppression distribution function to obtain a noise suppression distribution; and the noise data obtained by the noise component identification / separation unit to obtain the noise suppression distribution obtained by the noise suppression distribution calculation unit By multiplying the noise suppression component image data, the noise suppression component image data calculated by the noise suppression component calculation unit from the sharpness enhanced image data is subtracted by scaling. A noise suppression arithmetic processing unit for obtaining processed image data. There is provided an image processing apparatus for image noise suppression.

Further, in order to achieve the above object, the present invention provides a procedure for performing sharpness enhancement processing on original image data and creating sharpness enhanced image data in which noise included in the image is sharpened together with the image. Performing a smoothing process on the original image data to create smoothed image data, and subtracting the smoothed image data from the sharpness-enhanced image data to perform sharpness-enhanced edge and sharpness-enhanced subject image. Generating mixed image data containing mixed noise and noise, a step of performing edge detection from the original image data to obtain edge intensity data for identifying a subject edge region and a noise region, and a process of generating noise from the edge intensity data. A procedure for obtaining a weighting coefficient of the noise area indicating the degree of the area; and A procedure for obtaining noise data by multiplying the noise area by a weighting coefficient, setting a noise suppression distribution function representing the extent of noise suppression in the original image data, and performing convolution integration of the noise data and the noise suppression distribution function. Calculating the noise suppression component image data by multiplying the noise data by the noise suppression distribution, and scaling the noise suppression component image data from the sharpness enhanced image data. And providing a computer-readable recording medium having recorded thereon a program for causing a computer to execute a procedure of subtraction and subtraction to create an image in which noise components in a noise region of an original image are selectively suppressed. It is.

Here, the noise means not only the granularity caused by the particles of the photosensitive material included in the image data obtained by reading a photographic film or the like using a silver halide photosensitive material with a film scanner, but also Noises included in image data obtained by using an image pickup device such as a CCD or a MOS such as a digital still camera without using a silver halide photosensitive material and various image pickup tubes are also widely included.

According to the invention, an edge is detected from an original image, an edge strength is obtained, an area having a low edge strength is regarded as a noise area, and a weight coefficient of a noise area for dividing the noise area from the edge area is determined. Calculating the sharpness-enhanced image and the smoothed image from the original image, and subtracting the smoothed image from the sharpness-enhanced image to obtain a mixed component of the sharpness-enhanced edge and the sharpness-enhanced noise; From the noise component obtained by multiplying the mixed component of the edge and the noise by the weighting coefficient of the area, a noise suppression distribution is obtained using a noise suppression distribution function, and a noise suppression component is obtained from the noise suppression distribution and the noise component. By subtracting the noise suppression component from the sharpness enhanced image, for example, the original image data When read with a film scanner from a photographic film or the like made of a material, the particles are large settlements of the photosensitive material, and the granular mottle that forms the noise component is greatly suppressed in the part where the granular mottle is large (coarse). In a small portion of the granular mottle, the granularity is weakly suppressed or suppressed. Thereby, fluctuation of noise such as graininess can be suppressed and made uniform, and a high-quality image can be obtained in which a spatially fluctuating portion such as a granular mottle is selectively suppressed in a noise region of the image. . When the original image data is obtained by photographing using an image sensor such as a digital still camera, the photon fluctuation of the incident light, the inherent noise of each optical sensor in the image sensor, or an electric circuit is generated. Various noises such as thermal noise (noise) and quantization noise appear as pixel-wise signals or density fluctuations in the image, but the noise fluctuations are spatially sparse or dense, and Like the granular mottle of the silver halide photosensitive material, it forms a noise mottle, so that the noise mottle is strongly suppressed and the noise mottle is weakly suppressed, so that the noise can be made uniform and the image quality can be improved. Good images can be obtained.

That is, in the image processing method for noise suppression according to the present invention, a noise component is identified, and a large and coarse noise such as a noise mottle in the noise component is more strongly suppressed, and a small noise is weakly suppressed. By performing a process that does not suppress the noise, a process of making the fluctuation of the image density due to the noise small and uniform (reducing the variation in the magnitude of the fluctuation) is performed. When the noise component is a granular component, the granular pattern is not subjected to a smoothing process as in the past, and a large granular mottle is made inconspicuous.
Fine granularity (finely spatially granular with small amplitude) as obtained when using a fine grain emulsion in a silver halide photographic material
Can be

[0019]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an image processing apparatus for carrying out the image processing method of the present invention will be described in detail based on a preferred embodiment shown in the accompanying drawings. “Noise” in the present invention refers to noise in general as described above. Further, "noise" is simply referred to as noise in an electronic imaging system such as a digital still camera, but is generally referred to as granular rather than noise in silver halide photographic materials. Therefore, in the following description, particularly when a silver halide photographic material is described as an example, "granular" is used instead of "noise". The "noise motor" described later includes a granular motor in a silver halide photographic material, in addition to a noise motor in which a noise fluctuation forms a spatially dense portion in an electronic imaging system such as a digital still camera.

FIG. 1 is a block diagram of a color image reproducing system which incorporates an image processing apparatus according to the present invention, reads a color image, performs the image processing method of the present invention, and outputs a color image. . FIG. 2 is a block diagram of an embodiment of an image processing apparatus that performs the image processing method according to the present invention. FIG. 3 is a flowchart illustrating an example of a processing algorithm of the image processing method according to the present invention.
In the following description, image data obtained from a silver halide color photographic image as a digital image will be described as a representative example.

As shown in FIG. 1, a color image reproducing system 10 reads a color image such as a color photographic image (a film image of a color negative film, a color reversal film or the like, or a photographed image of a digital camera or the like) and reads a digital input image. An image input device 12 for obtaining data, and input image data input from the image input device 12 are subjected to image processing for noise suppression and sharpness enhancement of a digital image according to the present invention together with required image processing to obtain processed image data I. _And an image output device 1 that outputs a color image such as a print image based on the processed image data I ₁ output from the image processing device 14.
6 is provided.

The image input device 12 creates digital color image data and outputs it as input image data to the image processing device 14. For example, a color (or monochrome) negative film or a color (or monochrome)
There are a film scanner device that reads digital color film images such as reversal films and creates digital image data, and a scanner device for reflective original documents that reads color reflective original images such as printed matter and reflection print images to create digital image data. Note that, in the present invention, a digital camera, an electronic still camera, a video camera,
Alternatively, a recording medium storing digital image data created by the above, for example, a semiconductor memory such as a smart media, a memory stick, a PC card, or an FD, Z
A driver that drives a magnetic recording medium such as an ip, a magneto-optical recording medium such as an MO or MD, or an optical recording medium such as a CD-ROM or a Photo-CD and reads the digital image data as a digital image data. A display device such as a CRT monitor or a liquid crystal monitor that displays a copied image, a computer such as a PC for image processing that performs image processing of the read or displayed digital image data entirely or partially or a computer such as a WS may be used. Good.

The image output device 16 is for outputting a color image in which a color input image such as a color photographic image is reproduced, based on the processed image data I ₁ output from the image processing device 14 as the final processed image data. Digital photo printers, copiers, electrophotographs, laser printers, inkjets, thermal sublimation type, T
Image output devices such as digital color printers of various types such as A, TV and C for displaying as soft copy images
Display devices such as an RT monitor and a liquid crystal monitor, and computers such as a PC and a WS can be used.

The image processing device 14 adjusts the color and tone (gradation) of the input image data from the image input device 12 to output it to the image output device 16 in a desired color and tone reproduction. The color / tone processing unit 18 for creating _IO and the original image data _IO processed by the color / tone processing unit 18 are the most characteristic part of the present invention, and are the noise suppression of the digital image of the present invention. display and noise suppression, sharpness enhancement image processing unit 20 to create an image processing method implemented processed image data I ₁ for sharpness enhancement, the reproduced image based on the image data color and tone reproducibility is adjusted Image monitor and image processing unit comprising an image processing parameter setting unit for setting parameters for performing various required image processing and image processing of the present invention. And a meter setting section 22.

Here, the color / tone processing section 18 performs color reproduction so that the reproducibility of the color and tone (gradation) of the input image data input from the image input device 12 is appropriately reproduced in the image output device 16. Conversion or color correction (including gradation conversion or correction) is performed to create original image data I _O for performing the image processing method of the present invention. Examples of processing performed here include, for example, Examples include various processes such as color (gray) conversion and correction, gradation correction, density (brightness) correction, saturation correction, magnification conversion, and compression / expansion of a density dynamic range.

Image monitor / image processing parameter setting unit 2
2 includes an image monitor and an image processing parameter setting unit, and displays an input image on the image monitor based on input image data input from the image input device 12,
Using this image monitor (for example, by GUI)
A color / tone processing unit 18 for input image data and a noise suppression image processing unit 20 for implementing the image processing method of the present invention
Are used to set parameters of various image processing performed by a data input device such as a mouse or a keyboard (not shown). Here, parameters to be set include correction coefficients, conversion coefficients, magnifications, and the like used in the above-described various processes, and various coefficients and constants necessary for implementing the image processing method of the present invention described in detail later. And the like.

A noise-suppressed image processing unit (hereinafter, simply referred to as the present image processing unit) 20 for implementing the image processing method of the present invention.
Is the original image data I _O created by the color / tone processing unit 18
To perform image processing of the noise suppression and sharpness enhancement, which is a feature of the present invention is intended to create a processed image data I ₁ is the final processed image data to be output to the image output device 16.

Here, as shown in FIG. 2, the main image processing section 20 performs a sharpness enhancement process on the original image data I _O , and also includes noise (noise) including granularity included in the image together with the image. also creating a sharpened the sharpness enhanced image data I _S sharpness enhancement unit 24
When, by performing a smoothing process on the original image data I _O, the smoothing processing unit 26 to create a smoothed image data I _AV, by subtracting the smoothed image data I _AV from the original image data I _0, the object image Image data ΔI in which edges and noise are mixed
An edge / noise mixed component extraction unit 28 for creating an _EG and an edge detection of a subject image from the original image data _IO are performed.
An edge detecting section 30 for obtaining the edge intensity data E _O for identifying the object edge region and the noise region, the weighting factor of the noise region from the edge intensity data E _O W _G
And the mixed image data ΔI obtained by the mixed edge / noise component extraction unit 28
Multiplied by the weighting coefficient W _G of the noise region determined by the noise weighting factor calculation unit 32 in _EG, a noise component identifying separator unit 34 for obtaining the noise data G ₀ of the noise region, the spread of noise suppression in the original image data I _O A noise suppression distribution function setting unit 36 that calculates and sets a noise suppression distribution function s ₁ representing the noise suppression distribution function s ₁ , the noise data G ₀ obtained by the noise component identification / separation unit 34, and the noise suppression distribution set by the noise suppression distribution function setting unit 36. Noise mottle suppression distribution calculation unit (corresponding to the noise suppression distribution calculation unit in the present invention) 38 which is obtained by calculating a noise motor suppression distribution (corresponding to the noise suppression distribution in the present invention) M by performing convolution integration with the function s _1.
Multiplying the noise data G ₀ obtained by the noise component identification / separation unit 34 by the noise mottle suppression distribution M obtained by the noise mottle suppression distribution calculation unit 38, thereby calculating the noise suppression component image data G _1. Unit 40 and the sharpness enhanced image data I _S obtained by the sharpness enhancement processing unit 24 by scaling the noise suppression component image data G ₁ calculated by the noise suppression component calculation unit 40.
And a noise suppression arithmetic processing unit 42 for converting the image data into the processed image data I ₁ suitable for the image output device 16.

The noise suppression / sharpness enhanced image processing section 20 shown in FIG. 2 is basically configured as described above.
Next, referring to the flowchart shown in FIG. 3 showing the processing algorithm of the image processing method of the present invention,
The image processing method of the present invention will be outlined based on the operation of 0.

In this embodiment, as shown in FIG.
First, each pixel from the original image data I _O, performs sharpness enhancement in sharpness enhancement unit 24 (step 100) to obtain a sharpness enhanced image data I _S, performs smoothing processing in the smoothing processing unit 26 (step 102), the smoothed image data I _AV is obtained, and the mixed image data ΔI _{EG in} which the sharpness-enhanced edge and noise are mixed is extracted in the edge / noise mixed component extraction unit 28 (step 104).

On the other hand, the edge detection unit 30
Data I_OFrom the object edge area and the noise area
Strength data E for_OEdge detection
(Step 106), the noise area weighting coefficient calculation unit 32
, The weighting coefficient W of the noise region_GTo calculate
(Step 108). In addition, noise component identification and separation
The unit 34 identifies and separates noise data (scan
Step 110). That is, the mixed image data ΔI_EGTo
The weight of the noise area calculated by the noise weight coefficient calculation unit 32
Finding coefficient W_GMultiplied by the noise data G ₀Ask for.

Next, the noise suppression distribution function setting unit 36
And the original image data I₀The spread of noise suppression in
Represents the noise suppression distribution function s₁Set the noise mottle suppression
In the distribution calculator 38, the noise data G₀And this noi
Noise suppression distribution function s₁Convolution integral with
A torque suppression distribution M is obtained (step 112), and noise suppression is performed.
In the component calculation unit 40, the noise motor suppression distribution M
Is Data G₀Is multiplied by
Minute image data G₁(Noise mottle suppression
(Calculation of distribution) (step 114), noise suppression
In the arithmetic processing unit 42, the sharpness enhancement processing unit 24
Obtained sharpness enhanced image data I _SCalculated first
Noise suppression component image data G₁Is scaled and subtracted
(Calculate the noise suppression image) and, if necessary,
To convert the image data into image data suitable for the image output device 16.
And the processed image data I₁(Step 11
6).

Next, each of the above-described steps of the image processing method of the present invention will be described in detail. First, the sharpness enhancement step (step 100) will be described. Here, as a method of enhancing the sharpness of the image, an unsharp masking (USM) or a Laplacian (L
aplacian) is well known. Also in the present invention, by using these, the sharpness of the image can be enhanced if the sharpness of the image is slightly degraded.

The unsharp mask averages or blurs I ₀ (x, y) from original image data I ₀ (x, y) (pixel positions of interest are x and y) as shown in the following equation. Image I
_av x, y) and the edge enhancement component I ₀ (x, y) −I
_av (x, y) is multiplied by a coefficient a and added to the original image data I ₀ (x, y) to obtain a sharpness-enhanced image I _S (x, y) as in the following equation (4): It is. I _S (x, y) = I ₀ (x, y) + a [I ₀ (x, y) −I _av x, y)] (4) where a is a constant for adjusting the degree of sharpness enhancement. . Laplacian, the original image data I ₀ (x, y) second derivative of (Laplacian) ▽ ² I ₀ (x, y) of the original image data I ₀ (x,
A method of emphasizing sharpness by subtracting from y) is expressed by the following equation. I _S (x, y) = I ₀ (x, y) − ▽ ² I ₀ (x, y) (5) As a specific example of the sharpness enhancement by Laplacian, 3 × such as the following equation (6) is used. A coefficient array of 3 is often used. 0 -1 0 -1 -1 -1 1 -2 1 -1 5-1 -1 9-1 -1 -2 5 -2 (6) 0 -1 0 -1 -1 -1 1 -2 1

In this coefficient array, an unnatural contour is likely to occur at the edge of an image when particularly strong sharpness enhancement is applied. Therefore, in order to reduce such a drawback, in the present invention, the normal distribution type (Gau
It is preferable to use an unsharp mask using an ssian) blur function. G (x, y) = (1 / 2πσ ² ) exp [− (x ² + y ² ) / 2σ ² ] (7) where σ ² is a parameter representing the spread of the normal distribution function, the ratio of the values at the center x = 0 of the value and the mask in the _{x = x 1, G (x} 1, 0) / G (0,0) = exp [-x 1 2 / 2σ 2] (8) 0.1 By adjusting to be ~ 1.0,
The sharpness of the 3 × 3 unsharp mask can be made desired. When the value of equation (8) is set to a value close to 1.0, a mask substantially the same as the central Laplacian filter of equation (5) can be created. In order to change the sharpness of the mask, there is another method of changing the size of the mask. For example, by using a mask such as 5 × 5, 7 × 7, 9 × 9, etc. The spatial frequency can be changed significantly.

As the function form of the mask, a mask other than the normal distribution type described above, for example, an exponential function type mask as shown in the following equation (9) can be used. E (x, y) = exp [− (x ² + y ² ) ^1/2 / a] (9) Here, a is a parameter representing the spread of the unsharp mask as in σ ^{2 in} equation (8). There, the central value of the values of end-mask mask ratio, and _{E (x 1, 0) /} E (0,0) = exp [-x 1 / a] (10) is 0.1 to 1.0 By adjusting to become
The sharpness of the 3 × 3 unsharp mask can be made desired. In equation (10), E (x ₁ , 0) / E (0,0) =
A numerical example of the mask of the exponential function of Expression (9) when 0.3 is set is shown. 0.18 0.30 0.18 0.30 1.00 0.30 (11) 0.18 0.30 0.18 When one example of the unsharp mask is calculated from this mask, the following equation (12) is obtained. −0.12 −0.22 −0.12 −0.21 2.32 −0.21 (12) −0.12 −0.21 −0.12

Using such an unsharp mask,
Original image data I₀Sharpness-enhanced image data from (x, y)
I_S(x, y) can be obtained. In addition, used in the present invention
Unsharp mask and sharpness enhancement method
Is not limited to the above,
Conventionally known unsharp mask and spatial frequency filter
That sharpness enhancement methods such as
Of course. FIG. 4A shows that the edge component is dominant.
Area E₁And region E_TwoAnd the area with the noise mottle
Original image having a noise area including area A, area B and area C
Data I ₀An example of a one-dimensional profile waveform of
Apply sharpness enhancement to this profile waveform
As a result, as shown in FIG.₁And
And region E_TwoIn areas with strong edge components, such as
Signals are emphasized, and regions A, B and
The noise component is also emphasized in the noise area including the area C.
You can see it.

Next, the smoothing step (step 102) will be described. Examples of the method of performing the smoothing include processing in the real space domain and processing in the spatial frequency domain.
In the real space area processing, a method of calculating the average value by calculating the sum of all adjacent pixels and replacing the average value with the calculated value, a method of multiplying each pixel by a weighting factor, for example, a function of a normal distribution type to obtain an average value, a method of median filter There are various methods such as a method for performing such non-linear processing. On the other hand, in the processing in the spatial frequency domain, there is a method of applying a low-pass filter. For example, in the averaging method using the weight coefficient, the following equation (13) is used.
Can be mentioned. Here, (x, y) and the like represent the position coordinates of the pixel of interest in the image.

(Equation 1)

Where n is the mask size for averaging, w
(x, y) is a weight coefficient. If w (x, y) = 1.0,
Simple average. In the present invention, in the real space domain processing,
A method of multiplying a normal distribution type weighting factor to obtain an average value is used, but the present invention is not limited to this. At this time, it is preferable to use a mask of the following n × n pixels as a processing mask. Specifically, 3 × 3 to 5 × 5, 7 × 7, 9 ×
It is preferable to use about nine. w ₁₁ w ₁₂ w ₁₃・ ・ ・ ・ ・ w _1n w ₂₁ w ₂₂ w ₂₃・ ・ ・ ・ ・ ・ ・ w _2n w ₃₁ w ₃₂ w ₃₃・ ・ ・ ・ ・ ・ ・ w _3n・ ・ ・ ・ （14） ・ ・ ・ ・w _n1 w _n2 w _n3 ... w _nn

An example of a mask of 9 × 9 pixels is shown. In this equation (15), the center value is represented by a value normalized to 1.0, but in actual processing, the sum of the entire mask is set to 1.0. 0.09 0.15 0.22 0.28 0.30 0.28 0.22 0.15 0.09 0.15 0.26 0.38 0.47 0.51 0.47 0.38 0.26 0.15 0.22 0.38 0.55 0.69 0.74 0.69 0.55 0.38 0.22 0.28 0.47 0.69 0.86 0.93 0.86 0.69 0.47 0.28 0.30 0.51 0.74 0.93 1.00 0.93 0.74 0.51 0.30 (15) 0.28 0.47 0.69 0.86 0.93 0.86 0.69 0.47 0.28 0.22 0.38 0.55 0.69 0.74 0.69 0.55 0.38 0.22 0.15 0.26 0.38 0.47 0.51 0.47 0.38 0.26 0.15 0.09 0.15 0.22 0.28 0.30 0.28 0.22 0.15 0.09

Using such a mask, the smoothed image data I _AV (x, y) can be obtained from the original image data I ₀ (x, y). Note that the smoothing method used in the present invention is not limited to the various methods described above, and it goes without saying that any conventionally known smoothing methods can be applied. FIG. 4C shows the result of processing the profile waveform shown in FIG. 4A by the smoothing process, and shows the noise component of the noise area including the area A, the area B, and the area C with the noise mottle. Are suppressed and smooth, and the edge components E ₁ and E ₂ are dominant.
Also, it can be seen that the edge signal is suppressed and a smooth profile waveform is obtained.

Next, the step of extracting edge / noise mixed components (step 104) will be described. The smoothed image data I _AV (x,
y) is subtracted from the sharpness-enhanced image data I _S obtained in the sharpness enhancement step (step 100) in accordance with the following equation (16), and a mixed component ΔI, which is microstructure data in which sharpness-enhanced edges and noise are mixed, is obtained.
Extract _EG (x, y). ΔI _EG (x, y) = I ₀ (x, y) −I _AV (x, y) (16) In FIG. 4D, the profile waveforms shown in FIGS. 4B and 4C are used. Thus, the profile waveform of the mixed component ΔI _EG (x, y) obtained in the edge / noise mixed component extraction step is extracted.

Next, an edge detecting step (step 106)
Will be described. Here, edge detection by a local distribution method will be described as a typical example, but the present invention is not limited to this.

When performing edge detection, first, density conversion is performed by the following preprocessing. The reason why such preprocessing is performed is to reduce noise having no correlation between the R image data, the G image data, and the B image data constituting the color image data, and to improve the accuracy of edge detection performed thereafter. . That is, as shown in Expression (17), the density values D _R , D _G , and D of the three colors R, G, and B of the original image data I ₀ (x, y)
_B is multiplied by weighting factors r, g, b, and the visual density (Visual dens
ity) is converted to D _V. The _{D V = (rD R + gD} G + bD B) / (r + g + b) (17) weighting coefficients, for example, r: g: b = 4 : 5: using a value such as 1. This conversion is performed to reduce noise having no correlation between R, G, and B, and to improve the accuracy of edge detection. The size range of the preprocessing array is 5 ×
It is preferable to use a pixel having about 5 or 7 × 7 pixels. However, it is preferable to use a small array in the array, for example, an array of about 3 × 3, to reduce the variation in image density in a predetermined array described later. This is to calculate while moving.

Note that the weight coefficients r,
g and b can be obtained as follows. The weighting factor is noticeable when observed visually (this is because
There is a view that it corresponds to the spectral visibility distribution),
That is, it is preferable to set the optimum value based on the idea that the weight coefficient of the image data of the color having a large contribution is large. Generally, an empirical weighting factor is obtained based on a visual evaluation experiment or the like, and the following values are known as general knowledge (known literature includes Takashi Noguchi,
"Good granularity evaluation method for psychological response", Journal of the Photographic Society of Japan, 57
(6), 415 (1994), which vary depending on the color, but indicate values close to the following ratios). r: g: b = 3: 6: 1 r: g: b = 4: 5: 1 (18) r: g: b = 2: 7: 1 Here, a preferable value of the coefficient ratio r: g: b If the range is defined as follows, r + g + b = 10.0 and b is 1.0
In this case, the value of g is preferably in the range of g = 5.0 to 7.0. Where r = 10.0−b−g
It is.

Next, an edge detecting step (step 10)
Edge detection by local variance of 6) will be described. Detection of edges, n _E from the image data of the visual density D _V
× N _{E While} moving the array of pixels, the image density fluctuation in the array is calculated using Equation (19), and the local variance σ, which is the local standard deviation for each position, is sequentially determined for each pixel of interest (x, y). , The edge of the subject in the image is detected. The size (n _E × n _E ) of the pixel array may be determined as appropriate in consideration of the detection accuracy and the calculation load. For example, it is preferable to use a size of about 3 × 3 or 5 × 5.

[0047]

(Equation 2) However, let the target pixel position be x and y, and D _V (x + in _E / 2−
1/2, y + jn _E / 2-1 / 2) calculates the local variance σ (x, y)
The density of an _E × n _E pixel array, <D _V (x, y)> is the average density of the array,

(Equation 3) It is.

From the original image data I _O (x, y), the local variance σ (x, y) shown in the above equation (19) is calculated to obtain the edge strength E _O (x, y) of the subject image. Is given by the following equation (2)
An expression represented by an exponential function such as 1) is used. E _O (x, y) = 1−exp [−σ (x, y) / a _E ] (21) where a _E is a coefficient for converting the value of the local variance σ (x, y) into edge strength. And a threshold σ _{T of the} local variance σ (x, y) allocated to the edge strength E _O = 0.5, a _E = −σ _T / log _e (0.5) (22) The value of σ _T needs to be an appropriate value depending on the noise and the magnitude of the signal of the contour of the subject.
For a (256 gradation) color image, a value in the range of 10 to 100 is preferable. If this conversion is created as a look-up table, the calculation time required for the conversion can be reduced.

The conversion equation for obtaining the edge strength E _O (x, y) is not limited to the above equation, and another equation can be used. For example, a Gaussian function such as the following equation may be used. E _O (x, y) = 1−exp ｛− [σ (x, y)] ² / a _E1 ² ｝ (23) where a _E1 is converted from σ (x, y) to E _O (x, y). Local variance σ to be assigned to E _O (x, y) = 0.5
(x, y) when the threshold value of the sigma _T, which is ^{_{a E1 2 = -σ T 2 /}} log e (0.5) (24). The value of σ _T is preferably in the range of 10 to 100 for a color image of 8 bits (256 gradations) for each color.

The edge strength data E _O (x, y)
_May be normalized with the maximum local variance data σ _Max to obtain normalized edge strength data E _O (x, y) of 0 or more and 1 or less, as in the following Expression (25). E ₀ (x, y) = σ (x, y) / σ _Max (25) where σ _max is the maximum value of the local variance data σ (x, y), and σ (x, y) is normalized. Is a constant for The method for determining σ _Max is to find the maximum value from the local variance data σ (x, y) of the entire image obtained by Expression (19) as in Expression (26) below. σ _Max = Max ｛σ (x, y)｝ (26)

In addition, instead of using the maximum value obtained from the entire image, a part of the image, for example, a specific range of the central part of the image in which there is a high probability that there is an important subject in the image, or image data thinned out from all the images (the original image data 1/4 to 1/10
Degree) from the above equation (25) or (26).
You may ask for _Max . In this case, the image data of a specific range in the central portion of the image or the thinned image data is obtained by various processing condition adjustment original image data (press image data) which can be obtained in advance at a coarse pixel density before obtaining the processed image data by performing image processing. (Can image data) or may be extracted from the original image data. More preferably, σ _Max is an average value of values included within the upper 5% to 10% when local variance data σ (x, y) is arranged in descending order, for example, the upper 1
Average value of values included within 0% <σ (x, y)> _Max10% to σ
_Max , and when σ (x, y) exceeds this σ _Max , all are replaced with σ _Max . In this case, the average value may be an average value of the entire image, an average value of a predetermined range in a central portion where an important subject is often photographed, or an average value of thinned image data.

Incidentally, the edge detection method in the present invention is not limited to the above-described edge detection method of the local dispersion method, and other edge detection methods can be used. Edge detection methods other than the above-described local dispersion method include methods based on first-order differentiation and second-order differentiation, and there are some more methods for each. First, as a method based on the spatial first derivative, there are the following two operators. There are Prewitt operators, Sobel operators, and Roberts operators as differential edge extraction operators. Rober
The operator of ts can be expressed by the following equation. g (i, j) = ｛[f (i, j)-f (i + l, j + l)] ² + [f (i + l, j)-f (i,
j + l)] As a ² ｝ ^1/2 template type operator, use a 3 × 3 template corresponding to an edge pattern in eight directions Robinson
Operators and Kirsh operators. Next, as a method based on the spatial second derivative, there is a method using Laplacian. In this case, noise is emphasized. Therefore, a method of first performing normal distribution type blur processing and then detecting edges is often used.

FIG. 4E shows the waveform of the edge intensity data E ₀ obtained in the edge detection step from the original image data I ₀ shown in FIG. 4A. In the edge strength data E ₀ shown in FIG. 4A, it can be seen that the value of the edge strength data E ₀ increases near the regions E ₁ and E ₂ .

Next, the process of calculating the noise area weighting coefficient (step 108) will be described. Using the edge intensity data E _O (x, y) obtained in the edge detection step (step 106), a weighting coefficient W _E (x, y) of the edge area is obtained according to the following equation (27), and then the noise area is calculated. weighting coefficient W _G (x, y), and obtained in accordance with the following equation (28). _{W E (x, y) =} 1-α E + α E E 0 (x, y) (27) W G (x, y) = 1-W E (x, y) (28)

Here, α_EIs the edge region and the noise region
This is a constant for setting weighting.
The following arbitrary values can be set. Edge area
Weighting coefficient W_E(x, y) is 1-α_EWith a value between 1 and
N, W _EThe larger (x, y) is,
Is determined to have a high probability of being an edge area of_E(x, y)
Is smaller, the probability that the pixel position is a noise area is
It is determined to be high. Normalized edge strength data E₀
In the edge region where the value of (x, y) is close to 1.0, α_ETo the value of
Regardless, the weight coefficient W of the edge area_E(x, y) is 1.
It becomes a large value close to 0, and the weighting coefficient W of the noise region
_G(x, y) is the smallest. On the other hand, the normalized edge
Strength data E₀As (x, y) becomes smaller than 1.0,
Edge area weighting coefficient W_E(x, y) is the minimum value 1-α_E
, And the weighted data W of the noise region_G(x, y) is
Large value α_EApproach. Such α_EDefaults to a constant value.
The default value is set in advance, and the operator
While looking at the processed image processed by the default value, α
_EMay be adjusted as needed. What
Contact, α_EIs 1, the normalized edge intensity data E
_O(x, y) itself is the weight coefficient W of the edge region._E(x, y) and
1.0-E_O(x, y) is the noise area weighting factor
W_G(x, y).

FIG. 4F shows the weighting coefficient W of the noise area obtained from the edge strength data shown in FIG.
The waveform of _G (x, y) is shown. In the vicinity of the regions E ₁ and E ₂ , the value of the noise region weighting coefficient W _G (x, y) is small. Note that the value of the weighting coefficient W _G (x, y) of the noise region in the vicinity of the regions E ₁ and E ₂ is calculated by the above equation (2)
It is determined by a constant α _E that sets the weight of the edge region and the noise region set in 7).

Next, the noise data identification / separation step (step 110) will be described. The mixed component ΔI _EG (x, y) in which the sharpness-enhanced edge component and the noise component are obtained in the edge / noise mixed component extraction step (step 104) is expressed by the following equation (29): Noise weighting coefficient calculation step (step 10)
The noise data G ₀ (x, y) is obtained by multiplying the noise area weighting coefficient W _G (x, y) obtained in 8). G ₀ (x, y) = ΔI _EG (x, y) × W _G (x, y) (29) FIG. _4G shows the mixed component ΔI _EG shown in FIG.
noise data G ₀ (x, y) obtained by multiplying (x, y) by the weight coefficient W _G (x, y) of the noise region shown in FIG.
profile waveform y) is shown, it can be seen that the edge component is the signal component of the edge area dominated E ₁ and region E ₂ is removed.

Next, the noise motor suppression distribution calculation step (step 112) will be described. First, a noise suppression distribution function s (r) representing the extent of noise suppression in pixels in the noise region of the original image is set as in equation (1). Here, r is the distance from the origin P of the target pixel located at the coordinates (x, y) where the point P is the center position (origin) of the noise suppression spread. s (r) = exp (−r / a) (1) That is, s (x, y) = exp (− (x ² + y ² )
^(1/2) / a).

Here, a in the above equation (1) is a suppression range constant for adjusting the range of the noise suppression spread in the pixels of the original image, and is given in advance or set by input. When the suppression range constant a is set by input,
It is set by determining the position of the boundary of the range in which noise suppression is performed, that is, the suppression distance r _s and the value s _min at this position. That is, the suppression range constant a is calculated from the value s _min at the suppression distance r _s by the following equation (30). _{a = -r s / log (s} min) (30)

Where s_minIs between 0.1 and 0.5
Is preferably below, and the suppression distance r _sIs between 1 and 15
It is preferable that the value be in the following range. In particular, the film
Scan the image recorded in the
When obtaining the data, the suppression distance r_sThe value of
Format (image size of one frame of film) and fill
Film granularity (granularity
Tor) and scan the film to create a digital image.
Depending on the scanning aperture size of the scanner that obtains the image data
It is preferable to change it appropriately. That is, coarse granular
(The granular mottle is also large), the suppression distance r_sLarge value of
In the case of fine grain, the suppression distance r_sThe value of
Frustrate In addition, the size of the scanner
It is important to make the appropriate changes in the same
Scanner aperture of 15μm □ and 10μm □
, Even if the value of the suppression range constant a is the same,
The image density is 1.5 times and the pixel density of the 10μm square image is
This is because the roughness increases and the granular roughness changes.

The suppression distance r _s is obtained by increasing the mask size of the averaging in the smoothing step (step 102) by increasing the noise data G ₀ obtained in step 110.
Because spread component of Noizumotoru also blur increases contained in, it is necessary to increase the suppression distance r _s in order to suppress components of the Noizumotoru. Therefore, according to mask size for averaging the smoothing step (step 102), it may be changed appropriately be suppressed distance r _s. The mask size for averaging in the smoothing process is set to a minimum mask size of about 3 pixels × 3 pixels, and 11 pixels × 11
The maximum mask size is about the pixel, but the suppression range constant a
, The mask was 1.5 times to 2 times larger than the size, for example, when the mask size is 3 × 3, the value of the suppression distance r _s preferably about 5.

A function such as equation (2) may be set as the noise suppression distribution function s (r). s (r) = exp (−r ² / a ² ) (2) where r is the distance from the center of the noise suppression spread. In this case, the suppression range constant a is the suppression distance r _s
And the value s _min at that position using the following equation (31). _{a = r s / [- log} (S min)] (1/2) (31) or may set the function shown in Equation (3) as a noise suppression distribution function s (r). s (r) = rect (r / a) (3) where rect (r / a) is a rectangular function having a value of 1, and r is the distance from the center position of the noise suppression spread. . In this case, suppression range constant a, and suppressing the distance r _s to be set. Also, in Equations (2) and (3), s _min
By setting = 1, the same noise suppression distribution function s (r) as in the equation (1) can be obtained.

The noise suppression distribution function s (r)
Is normalized to a noise suppression distribution function s ₁ (x, y) equal to or more than 0 and equal to or less than 1 according to the following equation (32). s ₁ (x, y) = s (x, y) / S (32) Here, S is an integrated value obtained by the following equation (33).

(Equation 4) FIG. 4H shows an example of the noise suppression distribution function s ₁ (x, y) obtained by normalizing the noise suppression distribution function s (x, y) obtained using Expression (1) according to Expression (33). Is shown.

Next, using the normalized noise suppression distribution function s ₁ (x, y) and the noise data G ₀ (x, y), the noise suppression range is given by the following equation (34). Convolution integration is performed to calculate a noise motor suppression distribution M (x, y).

(Equation 5) The noise mottle suppression distribution M (x, y) calculated here is obtained by convolution and integrating the degree of suppression of noise at a given pixel position (x, y) received from other pixels. ₀ in the case where the image data read by a scanner or the like from the film image, which corresponds to the diffusion distribution of development inhibiting substance produced during the development of the silver halide grains of the film photosensitive layer, the noise data G ₀ The variation is regarded as the distribution of particles of the photosensitive material, and the noise mottle suppression distribution M (x, y) included in the original image data I ₀ and corresponding to the diffusion distribution of the development suppression substance generated around the silver particles of the photosensitive material. ).

Referring to the example shown in FIG. 4, the noise data G ₀ shown in FIG. 4G and the noise suppression distribution function s ₁ (x, y) shown in FIG. ), A noise mottle suppression distribution M (x, y) as shown in FIG. 4 (i) is obtained. In FIG. 4 (i), the value of the noise mottle suppression distribution is large in the portions shown in the region A, the region B and the region C. In the region A, the region B and the region C, the value of the silver halide photosensitive material is small. In this case, it means that the concentration of the development inhibiting substance generated in the silver salt photosensitive material is high. In this way, the noise motor suppression distribution M (x, y) is calculated.

Next, the noise motor suppression component calculation step (step 114) will be described. In the noise mottle suppression component calculation step, the noise data G ₀ calculated in step 110 is multiplied by the noise mottle suppression distribution M (x, y) calculated in step 112 as shown in the following equation (35). And noise suppression component image data G ₁ (x, y). G ₁ (x, y) = G ₀ (x, y) × M (x, y) (35)

The multiplication of the noise data G ₀ by the noise mottle suppression distribution M (x, y) is performed in the case of a silver halide photosensitive material in accordance with the action of a development suppressing substance and silver particles generated in the silver halide photosensitive material. This is to consider suppression. In the example shown in FIG. 4, the noise suppression component image data G ₁ (x,
y) is indicated. When the noise suppression component image data G ₁ (x, y) shown in FIG. 4 (j) is compared with the noise data G ₀ shown in FIG. 4 (g), in the area A, the area B and the area C,
Other regions for example regions E ₁ and region E _2,
It can be seen that the value is relatively large.

Finally, the noise suppression image calculation step (step 116) will be described. In the noise suppression image calculation process, the noise suppression component image data G ₁ (x, y) obtained in step 114 is scaled using the noise suppression coefficient α as shown in the following equation (36), and then the step 100 is performed. The processed image data I ₁ ′ is obtained by subtracting from the sharpness-emphasized image data I _S obtained in step ( ₁₎ . I ₁ ′ (x, y) = I ₀ (x, y) −α × G ₁ (x, y) (36) Here, the noise suppression coefficient α is a configurable parameter for controlling the degree of noise suppression. Yes, and are set and input as appropriate. Alternatively, default setting values may be provided in advance and changed as needed. Thereafter, conversion of image data suitable for the image output device 16 is performed to obtain the processed image data I ₁ from the processed image data I ₁ ′.

In the example shown in FIG. 4, a profile waveform of the processed image data I ₁ ^′ as shown in FIG. 4 (k) can be obtained, and the noise is reduced in the area of the noise motor such as the area A, the area B and the area C. It can be seen that the noise is largely removed and the noise is appropriately removed in other noise regions. Moreover, as compared with the original image data I ₀ shown in FIG. 4 (a), the edge region of the region E ₁ and region E ₂ are sharpness enhancement.

The above-described image processing method uses a granular component, which affects a certain pixel area, such as a granular component in a photographic light-sensitive material. Although effective as image processing for suppression, the above-described image processing method is also effective in digital cameras and the like that use a CCD or MOS imaging device that is not affected by noise components of other pixels. Since flaw defect correction and the like are performed using image data, it can be applied effectively. As the CCD image pickup device, the one having a honeycomb arrangement described in JP-A-10-136391 is also applicable.

As an image processing method for noise suppression, Japanese Patent Application Laid-Open No. H11-250246 proposes a method of suppressing noise by multiplying a noise component by a compression coefficient obtained from the edge strength of an image. In the noise region, a constant compression coefficient is applied, so that the amplitude of the noise is proportionally compressed. Also, by adopting a compression coefficient that increases the compression ratio as the noise amplitude increases, that is, by performing a non-linear conversion on the magnitude (amplitude) of the noise fluctuation,
The fluctuation of noise can be set in a more uniform direction than the image processing method described in the above publication. However, the image processing method of the present invention is different from the above two methods in that a noise amplitude is spatially formed with a large fluctuation of a noise component, unlike a noise mottle, in which the noise amplitude is suppressed. For example, this is a process of performing noise suppression on the region A, the region B, and the region C shown in FIG. 4A. In a process where noise fluctuation is large, the noise fluctuation can be further suppressed, and the noise fluctuation can be made uniform. .

The image processing method of the present invention is described as above. Such an image processing method may be configured as the above-described image processing apparatus including a circuit and hardware, or may be a program that performs functions in a computer as software. , Computer-readable recording medium recording a program for executing the above method, CD = ROM
It may be provided as such.

When such an image processing method of the present invention is applied to a silver halide color photograph, for example, a photographic image photographed on a 35 mm color negative film, a remarkable improvement effect in both graininess and sharpness can be obtained at a glance. I was able to. In particular, since the particles have a processing effect comparable to the improvement of the granularity of the photosensitive material by making the photosensitive material finer, there is a drawback of various types of granular removal processing methods based on the conventional averaging and reduction of fluctuation. The sense of incongruity has disappeared.
Further, with respect to sharpness, by combining with the above-described graininess suppression, a greater emphasis effect was obtained than with a conventional unsharpness mask or a Laplacian filter without deteriorating graininess. Further, when applied to an image taken by a digital still camera, a remarkable image quality improvement effect was obtained as in the case of the color image based on the silver halide color photograph.

The image processing method, the image processing apparatus and the recording medium of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments, and various improvements can be made without departing from the gist of the present invention. Of course, changes may be made.

[0075]

As described above in detail, according to the present invention, both the subject image and the noise are sharpened by first sharpening the original image, and the subject contour and the noise component are extracted from the image. , And selectively removes noise components, so that noise does not form large irregularities that appear blurry and visually unpleasant, and low-contrast image signals are not mistaken for noise. No unnatural artifacts are formed at the boundary of the emphasized area. Further, since the noise component is suppressed by extracting a region where the density fluctuation of the noise component is spatially coarse and selectively removing it largely, the processed image rarely includes a large coarse noise component such as a noise motor. (The density fluctuation is small.) Therefore, the density fluctuation is made uniform, the noise can be spatially fine, and the noise can be suppressed visually and naturally.

According to the present invention, when the noise is granular, the granularity is emphasized with sharpness, and a granular component such as a granular mottle having large spatial fluctuation is removed / suppressed, so that the granular pattern is refined and uniform. In silver halide photographic materials, fine grained particles are obtained when a fine grain emulsion is used, and visual discomfort and discomfort such as blurred grains, which is a drawback of the conventional method using smoothing, are obtained. There is no natural grain suppression effect. Further, by applying the image processing method of the present invention to a silver halide color photographic light-sensitive material, there is no so-called “blur grain” unnaturalness or unnaturalness, which is a drawback of the conventional grain suppression processing method, and the graininess is improved. Thus, an extremely remarkable improvement effect can be obtained, and a great industrial effect can be obtained.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an embodiment of a system in which an image processing apparatus according to the present invention is incorporated, reads a color photographic image, performs image processing for noise suppression and sharpness enhancement, and outputs a color image on an output device. is there.

FIG. 2 is a block diagram illustrating an embodiment of an image processing unit that performs image processing for noise suppression and sharpness enhancement in the image processing apparatus according to the present invention.

FIG. 3 is a block diagram showing one embodiment of the image processing method of the present invention.

FIGS. 4A to 4K are diagrams illustrating an example in which image data is processed by the image processing method of the present invention.

[Explanation of symbols]

 Reference Signs List 10 color image reproduction system 12 image input device 14 image processing device 16 image output device 18 color / tone processing unit 20 noise suppression image processing unit 22 image monitor / image processing parameter setting unit 26 smoothing processing unit 28 edge / noise mixed component extraction Unit 30 edge detection unit 32 noise area weight coefficient calculation unit 34 noise component identification and separation unit 36 noise suppression distribution function setting unit 38 noise mottle suppression distribution calculation unit 40 noise suppression component calculation unit 42 noise suppression calculation processing unit

──────────────────────────────────────────────────の Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) H04N 5/21 G06F 15/68 405 H04N 1/40 101D F-term (Reference) 5B057 CA01 CA08 CA12 CA16 CB01 CB08 CB12 CB16 CE02 CE03 CE05 DA20 DC16 DC19 DC32 5C021 PA73 RA02 RA06 XB02 XB03 YA01 YC13 ZA01 5C077 LL02 PP02 PP03 PP47 PP48 PQ12 PQ18 TT02 TT06 TT09 5L096 AA02 AA06 BA20 DA01 EA06 EA07 GA09 FA06 FA06

Claims (8)

[Claims] 1. An original image data is subjected to sharpness enhancement processing, and sharpness-enhanced image data in which noise included in the image is sharpened together with the image is created. A smoothing process is performed on the original image data to perform smoothing. Generating image data; subtracting the smoothed image data from the sharpness-enhanced image data to create mixed image data in which the edges of the sharpened image of the subject and the sharpness-enhanced noise are mixed; Edge detection is performed from the data to determine edge intensity data for discriminating between a subject edge region and a noise region.From this edge intensity data, a noise region weighting coefficient indicating the degree of the noise region is determined. The noise data is obtained by multiplying the noise area by a weighting coefficient. By setting a noise suppression distribution function representing the spread of noise suppression, obtaining a noise suppression distribution by performing convolution integration of the noise data and the noise suppression distribution function, by multiplying the noise data by the noise suppression distribution, Calculating noise suppression component image data, scaling the noise suppression component image data from the sharpness enhanced image data and subtracting the same to create an image in which noise components in the noise region of the original image are selectively suppressed. An image processing method for suppressing noise of a digital image. 2. The noise suppression distribution function is a monotonically decreasing function having a maximum value at a center position of a noise suppression spread in a pixel of an original image, and a value decreasing as the distance from the center position increases. The image processing method according to 1. 3. The image processing method according to claim 1, wherein the noise suppression distribution function is a rectangular function having a constant value within a range in which noise is suppressed in pixels of an original image. 4. The image processing method according to claim 2, wherein a range of the noise suppression distribution function in which the noise is suppressed in the pixels of the original image is a range of 1 to 15 pixels in number. 5. The noise suppression distribution function is set by determining a position of a boundary of a range in which noise suppression is performed in pixels of an original image and a value at this position.
The image processing method according to any one of the above. 6. The noise suppression distribution function, wherein r is a distance from the center position of the noise suppression spread in the pixels of the original image, and a is a suppression range constant that determines the range of the noise suppression spread in the pixels of the original image. Then, the following formulas (1) to
6. The image processing method according to claim 5, wherein the function is a function s (r) represented by any one of (3). s (r) = exp (−r / a) (1) s (r) = exp (−r ² / a ² ) (2) s (r) = rect (r / a) (3) where: Rect (r / a) in (3) is a rectangular function having a value of 1. 7. A sharpness enhancement processing section for performing sharpness enhancement processing on the original image data to generate sharpness enhanced image data in which noise included in the image is sharpened together with the image; and performing a smoothing processing on the original image data. A smoothing processing unit for performing smoothing image data, and subtracting the smoothed image data created by the smoothing processing unit from the sharpness-enhanced image data to obtain a sharpness-enhanced edge and a sharpness of the subject image. An edge / noise mixed component extraction unit that generates mixed image data in which the emphasized noise is mixed; and an edge that performs edge detection from the original image data to obtain edge intensity data for identifying a subject edge region and a noise region. A detection unit, and a degree of a noise region is indicated from the edge intensity data obtained by the edge detection unit. A noise region weighting factor calculating unit for obtaining a weighting factor for the noise region; and the mixed image data created by the edge / noise mixed component extracting unit multiplied by the noise region weighting factor obtained by the noise region weighting factor calculating unit. A noise component identification / separation unit that obtains noise data; a noise suppression distribution function setting unit that sets a noise suppression distribution function that represents the extent of noise suppression in the original image data; and the noise that is obtained by the noise component identification / separation unit. A noise suppression distribution calculation unit that performs convolution integration of data and the noise suppression distribution function set by the noise suppression distribution function setting unit to obtain a noise suppression distribution; and the noise suppression distribution obtained by the noise suppression distribution calculation unit. Is multiplied by the noise data obtained by the noise component identification / separation unit, A noise suppression component calculation unit that calculates noise suppression component image data; and a noise that obtains processed image data by scaling and subtracting the noise suppression component image data calculated by the noise suppression component calculation unit from the sharpness enhanced image data. An image processing apparatus for suppressing noise of a digital image, comprising: a suppression calculation processing unit. 8. A procedure for performing sharpness enhancement processing on the original image data to create sharpness-enhanced image data in which noise included in the image is sharpened together with the image; and performing smoothing processing on the original image data. A step of creating smoothed image data; and subtracting the smoothed image data from the sharpness enhanced image data to create mixed image data in which edges of a subject image with sharpness enhanced and noise with sharpness enhanced are mixed. A step of performing edge detection from the original image data to obtain edge intensity data for identifying a subject edge region and a noise region; and obtaining a noise region weighting coefficient indicating a degree of the noise region from the edge intensity data. Multiplying the mixed image data by a weighting coefficient of the noise region, Obtaining a noise suppression distribution function, setting a noise suppression distribution function indicating a spread of noise suppression in the original image data, and performing a convolution integration of the noise data and the noise suppression distribution function to obtain a noise suppression distribution, A computer performs a procedure of calculating noise suppression component image data by multiplying the noise data by the noise suppression distribution, and a procedure of scaling and subtracting the noise suppression component image data from the sharpness enhanced image data. A computer-readable recording medium having recorded thereon a program for creating an image in which noise components in a noise region of an original image are selectively suppressed.