JP2000175047A - Image processing method and image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing method for noise suppression and sharpness emphasis of a digital image where granularity is suppressed and image sharpness is emphasized without causing unnatural artifact resulting from discontinuity of a border of a granularity eliminating area and a sharpness emphasis area and to provide an image processor to execute this method. SOLUTION: Sharpness emphasis and smoothing are applied to an original image to obtain image data intermingled with an object image edge and granular data, edges are detected from the original image to obtain weighted data of a granular area, the weight data are multiplied by the intermingled image data consisting of the edges and the granular data to obtain granular data in a granular area for each color, a local granular coefficient representing a spatial magnitude of granular density fluctuation and a characteristic of the magnitude of the fluctuation on the basis of the granular data in each color, a black/white granular component and a color granular component are identified and separated, and a black/white granular suppression component and a color granular suppression component are obtained by multiplying respective suppression coefficients by the black/white granular component and the color granular component. Then the black/white granular suppression component and color granular suppression component are selectively eliminated from the sharpness emphasis image.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method and apparatus for suppressing digital image graininess and sharpness enhancement of a digital image, which suppresses noise (noise) such as graininess of the digital image. .

[0002]

2. Description of the Related Art In a digital image in which an image such as a silver halide photograph is recorded by an image input scanner and output by an image output printer, the sharpness is greatly deteriorated by the scanner and the printer. Sharpness enhancement is performed using a Laplacian filter or an unsharp mask (USM). However, the sharpness of the image is improved, and at the same time, image noise such as graininess in the image and electrical noise (noise) from a scanner or the like is emphasized, which has a side effect of worsening the graininess and noise. Since it gives an unpleasant sensation, there is a drawback that only a modest sharpness enhancement can be performed within a range where deterioration of graininess and noise is allowed, that is, within a range that does not visually discomfort.

In particular, a silver halide color photographic light-sensitive material used for photography contains undeveloped silver halide particles and developed silver particles in addition to cyan, magenta and yellow dyes, for example, a European patent (Japanese Patent No. EPC) 800114A
In the case of the silver halide color photographic light-sensitive material described in the embodiment of No. 2, the sharpness enhancement processing according to the above-described conventional technique or the digital image processing operation such as the color correction and the gradation correction is included in the image. The graininess due to the undeveloped silver halide particles and the developed silver particles is emphasized, and the image quality becomes unfavorable.

[0004]

By the way, there have been proposed some image processing methods for enhancing the sharpness of a digital image by removing grains that are noise. However, as a method of removing grains, The method of averaging or blurring the grain causes the blurred grain pattern to be visually uncomfortable, to remove the fine structure of the image that should not be removed together with the grain, and to make the image look unnatural. It has drawbacks such as the occurrence of certain artifacts, and is not suitable for aesthetic images such as photographs.

For example, in the above-described conventional grain suppression / sharpness-enhanced image processing method, sharpness is emphasized by an unsharp mask, grain is blurred or smoothed, and a grain (noise) signal is converted from an original image. And the contour signal are separated at the signal level, the contour signal is emphasized with sharpness, and the smooth area is grain-suppressed, so that the small signal is regarded as grainy and processed. That is, there is a problem that image signals such as grass and clothing texture and hair are suppressed together with graininess, and there is a disadvantage that an image processing artifact becomes a visually unpleasant image. That is, in such a conventional method, blurring and averaging are used as a method of suppressing graininess, and the blurred granular pattern looks as if the graininess has improved. Is recognized as an unpleasant pattern, and there is a problem that the face or skin of a portrait photograph or the like, or a uniform subject such as a wall or the sky stands out.

The present invention has been made in view of the above-mentioned drawbacks of the prior art, and is particularly useful for images such as photographs, prints, televisions, electronic still photographs, and various types of copying machines. In a photographic image containing silver halide particles and developed silver particles, noise inherent to the image, such as blurring by a camera, graininess or blurring of a photographic light-sensitive material, that is, noise and deterioration of sharpness (or sharpness), or the original image is When digitizing with an image input device, the above-mentioned disadvantages, that is, graininess is emphasized and visually uncomfortable, and image signals with low contrast are mistaken for graininess and are suppressed or eliminated. It does not cause any drawbacks or the disadvantage that the boundary between the grain removal area and the sharpness enhancement area becomes discontinuous and unnatural artifacts are seen in the image Suppresses graininess, and an object thereof to provide an image processing method and image processing apparatus for carrying out this for noise suppression and sharpness enhancement of a digital image for performing processing for emphasizing images sharpness.

[0007]

In order to achieve the above-mentioned object, the present invention first performs sharpness enhancement processing on original image data, and sharpens grains or noise contained in the image together with the image. Image data is created, a smoothing process is performed on the original image data, smoothed image data is created, and the smoothed image data is subtracted from the sharpness-enhanced image data. In the same manner, to create mixed image data of the subject image edge and the granularity in which the sharpness-enhanced granularity is mixed, and then perform edge detection from the original image data to identify the subject edge region and the granular region. The weighting data of the edge area and the weighting data of the granular area are obtained, and the mixed image of the subject image edge and the granularity is obtained. The data is multiplied by the weighted data of the granular region to obtain granular data of the granular region for each color. Subsequently, from the granular data of each color, characteristics of the spatial magnitude and the magnitude of the density variation due to the granularity are obtained. The black and white granular component and the pigment granular component are identified and separated, and the obtained black and white granular component and the pigment granular component are multiplied by the respective suppression coefficients to obtain the black and white granular suppressing component and the pigment granular suppression. Component, and finally, by selectively removing the black-and-white granular suppression component and the pigment granular suppression component from the sharpness-enhanced image data, graininess is suppressed, and sharpness enhancement in the image edge region is maintained. An object of the present invention is to provide an image processing method for suppressing noise and enhancing sharpness of a digital image, which is characterized by creating a processed image.

The present invention also provides a sharpness processing section for performing sharpness enhancement processing on original image data to create sharpness-enhanced image data in which granularity or noise contained in the image is sharpened together with the image; A smoothing processing section for performing smoothing processing to generate smoothed image data; and subtracting the smoothed image data from the sharpness-enhanced image data to form a sharpness-enhanced edge of the subject image and a sharpness-enhanced image. An edge / granular mixed component extraction unit that creates mixed image data of the subject image edge and the granular shape in which the extracted granularity is mixed, and an edge detection unit that performs edge detection from the original image data to identify a subject edge region and a granular region An edge detector for obtaining weight data of the edge region, and whether the weight data of the edge region And the particulate region weighting coefficient computing section for obtaining the weighting data of the granular area, the mixed image data with the subject image edges and particulate obtains the grain data granular area for each color is multiplied by the weighting data of the granular region,
From the granular data of each color, a spatial granularity of the density fluctuation due to the granularity and a local granularity coefficient representing the characteristics of the magnitude of the fluctuation are obtained, the black-and-white granular component and the pigment granular component are identified and separated, and the obtained black-white granular component is obtained. Multiplying each of the pigment and the granular component by the respective suppression coefficient, a granular component identification processing unit for obtaining a black and white granular suppressor and a pigment granular suppressor, and the black and white granular suppressor and the pigment granular suppressor from the sharpness emphasized image data And an output image calculation unit for creating a processed image in which sharpness enhancement in an image edge region is held by suppressing graininess by selectively removing graininess and sharpness enhancement of a digital image. And an image processing apparatus for the same.

Here, the original image data is digital image data recorded by an image recording device such as a scanner from an image photographed on a silver halide color photographic light-sensitive material. It is preferable that the particles include particles formed by at least one of developed silver particles. Also,
The original image data is digital image data recorded by an image recording device such as a scanner from an image photographed on a silver halide color photographic light-sensitive material, wherein the black and white grains are the undeveloped silver halide grains and the developed silver grains. In addition to the granularity of at least one, random noise of each color,
Preferably, the image recording device includes at least one of fixed pattern noise and moire due to aliasing.

Preferably, the edge detection is based on a local dispersion method, and the sharpness enhancement processing is a Gaussian type unsharp mask processing.
Preferably, the smoothing process is a Gaussian type mask process. Of course, these are not limited to the Gaussian type, but may be other types. In addition, it is preferable that sharpness enhancement be applied sufficiently and sufficiently even if graininess or noise is noticeable without graininess suppression.

In the present invention, first, a sharpness enhancement process is performed on an original color image to sharpen the image, and at the same time to sharpen both the granularity and noise (noise) contained in the image. The region is divided into regions and the flat portion is regarded as a granular region, and a granular signal is detected. next,
For example, from the granular signals of R (red), G (green), and B (blue),
By calculating the characteristic amount of the signal caused by the difference between the black-and-white granular form and the pigment granular form, the two are distinguished. By selectively removing the identified black and white grains from the grain areas of the three-color sharpened image signal, for example, black and white grain components due to undeveloped silver halide particles and developed silver particles of a silver halide color photographic light-sensitive material are removed. be able to. On the other hand, with respect to the remaining pigment particles, for example, particle suppression is performed for each of R, G, and B colors.

In the graininess suppression method applied to the image processing method and apparatus of the present invention, the graininess component as well as the subject component of the image is refined by sharpness enhancement, and the graininess component is subtracted from the sharpness-enhanced image. Is suppressed, it is possible to realize a grain that is spatially finer than the original grain and has a small change in shading. Accordingly, since the grains are spatially refined, fine grains such as those obtained when a fine grain emulsion is used in a silver halide color photographic light-sensitive material can be obtained.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An image processing method for suppressing noise and enhancing sharpness of a digital image according to the present invention and an image processing apparatus for implementing the method will be described in detail with reference to the accompanying drawings.

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 image processing for suppressing graininess and enhancing sharpness, 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, a color photograph image will be described as a digital image, and color data of three colors R (red), G (green) and B (blue) will be described as representative examples of color image data.
The present invention is not limited to this.

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. Image input device 12 for obtaining data
When, together with the required image processing on the input image data input from the image input unit 12 performs image processing for noise suppression and sharpness enhancement of a digital image of the present invention, an image processing apparatus to obtain the output image data I ₁ 14 And an image output device 16 that outputs a color image such as a print image based on the output image data I ₁ output from the image processing device 14.

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, an image recording device, specifically, a color (or monochrome) ) Film scanners that create digital image data by reading a color film image such as a negative film or color (or monochrome) reversal film with an image pickup device or image pickup device such as a CCD device, and a color reflection original such as a printed matter or a reflection print image. A scanner device for reflective originals that creates digital image data by reading an image with an image sensor, or an image capturing device, specifically, a digital camera that creates digital image data by directly capturing an object with an image sensor, an image capturing device, or the like. And electronic still cameras and video cameras Or, a recording medium storing the digital image data generated by these, for example, smart media, semiconductor memory and F such as a PC Card
A driver that drives a magnetic recording medium such as D or Zip, a magneto-optical recording medium such as MO or MD, or an optical recording medium such as CD-ROM or Photo-CD and reads the digital image data, and reads these digital image data CRT monitor to display soft copy image with
Examples of the display device include a display device such as a liquid crystal monitor, and a computer such as an image processing PC or WS for performing image processing of digital image data read or displayed in whole or in part.

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 output image data output from the image processing device 14 as final processed image data. Digital photo printers, copiers, electrophotographs, laser printers, inkjets, thermal sublimation, TA that output color hard copy images such as reflection print images and reflection original images
Image output devices such as various types of digital color printers, TVs and Cs 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 apparatus 14 which is a feature of the present invention comprises:
Color / tone processing for creating original image data _IO by adjusting the color and tone (gradation) of the input image data from the image input device 12 to output the desired color and tone reproduction to the image output device 14. And an image processing method for noise suppression and sharpness enhancement of a digital image according to the present invention, which is the most characteristic part of the present invention in the original image data _IO processed by the color / tone processing unit 18. granular suppressed or sharpness enhancement image processing unit 20 for creating the output image data I ₁ are carried out, an image monitor and various required image color and tone reproduction to display a reproduced image based on the image data adjusted An image monitor / image processing parameter setting unit 22 including an image processing parameter setting unit that sets parameters for performing processing and image processing of the present invention.

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 properly 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 data input device such as a mouse or a keyboard (not shown) for input image data showing parameters of various image processing performed by the color / tone processing unit 18 and the graininess suppression / sharpness-enhanced image processing unit 20 for implementing the image processing method of the present invention. It is for setting by. Here, the parameters to be set include a correction coefficient, a conversion coefficient, a magnification, and the like used in the above-described various kinds of processing, and a coefficient such as a coefficient necessary for performing the image processing method of the present invention described in detail later. Parameters and the like.

A grain suppression / sharpness-enhanced image processing unit (hereinafter, simply referred to as a main image processing unit) 20 for implementing the image processing method of the present invention includes a main image data I _O created by the color / tone processing unit 18. The final processed image data I which is output image data to be output to the image output device 16 after performing the graininess suppression / sharpness enhanced image processing which is a feature of the present invention.
It is used to create a _1.

Here, the image processing section 20 is shown in FIG.
Thus, the original image data I_OSharpness enhancement processing
Therefore, the granularity contained in this image together with the image or
Sharpness-enhanced image with sharpened noise (noise)
Data I_SA sharpness enhancement processing unit 24 for creating
Original image data I_OTo the smoothed image data.
Data I_LAnd a smoothing processing unit 26 for generating
Enhanced image data I_SFrom the smoothed image data I_LSubtract
Both the edge of the subject image and the sharpness
Image of subject image with mixed grain and mixed image of edge and grain
Data ΔI_EGEdge / granular mixed component extraction unit 2
8 and the original image data I_OEdge detection from the
Edge component for distinguishing between body edge region and granular region
E_O, And an edge component E_O
From the weighted data W of the granular area _GFind the granular area weight
Locating coefficient calculation unit 32 and edge / granular mixed component extraction unit
Mixed image data ΔI obtained in step 28_EGIn this granular area
Weighting data W_GAnd the granular data G of the granular area_O
Is determined for each of the colors R, G, and B, and the granular
Data G_O(Undeveloped) silver halide grains and developed silver grains
Differences between Black and White Granules and Pigment Clouds
Differences in signal fluctuations due to the
Size), and the black-and-white granular component G_AgAnd pigment granular component G
_DyeAnd each component has its own suppression coefficient
α_AgAnd α_DyeAnd black-and-white granular suppression component G '_AgAnd pigment particles
Condition suppressing component G '_DyeComponent identification processing unit 34 for determining
And the sharpness created by the sharpness enhancement processing unit 24.
Nest emphasized image data I_SFrom black-and-white granular suppression component G '_AgWhen
Pigment granular suppression component G '_DyeAnd selectively remove
Output image data to be output to the image output device 16
As the final processed image data I₁Create an output image
And a calculation unit 36.

The granularity suppression / sharpness enhanced image processing unit 20 shown in FIG. 2 is basically configured as described above.
The processing unit 2 will be described with reference to a flowchart shown in FIG. 3 showing a processing algorithm of the image processing method of the present invention.
0 and the image processing method of the present invention will be described in detail.

[0024] In the present invention, as shown in FIGS. 2 and 3, each color, first for each pixel, from the original image I _O, in the sharpness enhancement processing unit 24 the sharpness enhanced image I _S, the smoothing processing unit 26 , A smoothed image I _L is created, and the edge / granular mixture component extraction unit 28 extracts fine image data ΔI _{EG in} which edges and granularity (noise) are mixed. On the other hand, an edge component E _O , which is edge image data of a subject in the image obtained from the original image I _O by a method such as local dispersion, is detected by the edge detection unit 30, and the edge component obtaining weighted data W _G of the particulate region (x, y) from E _O.

In the granular component identification processing section 34, first,
Particulate domain weighting coefficients obtained in granular domain weighting factor calculation unit 32 data W _G (x, y) using a previously edge granulated mix was determined by edge particulate mixed component extractor 28 fine image data [Delta] I _EG ( x, y), the edge component E ₀ of the edge region and the granular component G ₀ of the granular region are identified, and the granular component G ₀ is separated and extracted for each of R, G, and B colors. Because this particulate component G ₀ and black-and-white particulate and the dye particulate made of silver particulate, such as undeveloped silver halide grains and developed silver grains are mixed, particulate component G ₀ may further form of black-and-white particulate and the dye particulate The numerical value of the local feature is converted into a local granularity coefficient G _L (x,
y) is determined, and based on this, the two are distinguished and separated into a black-and-white granular component G _Ag composed of silver particles and a pigment granular component G _Dye . Subsequently, it obtains a black-and-white particulate component G _Ag and the dye particulate component G _Dye and each suppression coefficient alpha _Ag and alpha _Dye multiplied by the black-and-white particulate suppressing component G _'Ag and the dye particulate suppressing component G' _Dye.

[0026] Finally, in the output image calculating unit 36, by subtracting the sharpness enhancement processing unit 24 the sharpness enhanced image data created by the I _S from the black-and-white particulate suppressing component G _'Ag and the dye particulate suppressing component G' _Dye, Image output device 16
As the output image data for outputting to the particulate is suppressed, it is possible to obtain a final processed image I ₁ of sharpness in the image edge region is emphasized.

The features of the present invention are as follows: i) First, a sharpness-enhanced image and a smoothed image are created from an original image, and image data with mixed edges and grains is created. ii) From the mixed edge / granular image data, the edge component and the granular component are identified using the weight coefficient of the granular region calculated from the edge image data separately obtained from the original image by a method such as local dispersion, and the granular component is identified. Extract the components. iii) The granular component further quantifies the morphological characteristics of black-and-white particles and pigment particles, identifies them based on this, and separates them into black-and-white granular components composed of silver halide particles and developed silver particles and pigment granular components. . iv) The graininess of the grainy region is suppressed by multiplying the black-and-white grain component and the pigment grain component by their respective suppression coefficients and selectively removing them from the sharpness emphasized image. At this time, since the image components in the edge area are held without being suppressed, it is possible to simultaneously suppress graininess and enhance sharpness.

Next, each step of the image processing method of the present invention will be briefly described with reference to FIG. 3 (and FIG. 2). 1) Sharpness enhancement process (Sharpness enhancement processing unit 2)
4), using an unsharp mask USM, a Gaussian type unsharp mask (Gaussian USM), or a Laplacian filter, performs emphasis on the original image I ₀ to recover the image blur and improve the sharpness.
_Ask for _S. 2) Step of extracting mixed edge / granular components (by the mixed edge / granular component extraction unit 28) For example, the original image _I0 is smoothed using averaging such as an nxn smoothing mask of the original image or a blur mask. The image I _L is created (smoothing step (by the smoothing processing unit 26)), and the fine image data ΔI _{EG including both} edges and grains is created from the sharpness enhanced image I _S.

3) Step of calculating weighting coefficients for edge area and flat area (by granular area weighting coefficient calculation unit 32) Edge E _{O of an} image subject is detected from original image I ₀ using an edge detection mask such as a local variance. (Edge detection unit 30
In accordance), obtaining the weighting function W _G flat (granular) area of particulate to be suppressed from the edge component.

[0030] 4) According to the edges and identification and separation step of the particulate (particulate component identification processing unit 34) to the fine image data [Delta] I _EG of destination obtained edge granular mix is multiplied by the weighting function W _G of the particulate region, particulate component determine the G _O. 5) Step of discriminating / separating the black-and-white granular component and the pigment granular component (by the granular component identification processing unit 34) From the granular component G ₀ of R, G, and B, from the black-and-white particulate and the pigment cloud by silver halide particles and developed silver particles The difference in signal variation (spatial magnitude and the magnitude of density variation) due to the difference in the form of the pigment particles is identified, and the black-and-white granular component G _Ag and the pigment granular component G _Dye are separated. 6) According to the calculation step (particulate component identification processing unit 34 of the inhibiting component black and white particulate and the dye granular) a multiplied by the respective inhibition constants alpha _Ag and alpha _Dye in black-and-white particulate component G _Ag and the dye particulate component G _Dye, respectively The black-and-white granular suppression component G _Ag ^′ and the pigment granular suppression component G _Dye ^′ .

[0031] 7) Step of calculating the sharpness enhancement image suppression black and white particulate and the dye particulate from step (final processed image) (depending on the output image calculating unit 36) from the image I _S subjected to sharpness enhancement, black-and-white granular obtained in granular region Inhibitor G _Ag ^' and pigment granular inhibitor G _Dye
By dividing ^' , the final processed image I ₁ is obtained in which the object edge region of the original image I _O is sharpened and the granular region is suppressed in granularity. In the present invention, by performing the above-described image processing, it is possible to enhance the sharpness of the edge region of the image and suppress the granularity of the flat region. In the present invention, the parameter for determining the degree of graininess suppression can also be automatically set based on the root mean square (RMS) of the density variation ΔD of graininess / edge components. In addition, the image processing algorithm of the present invention, which suppresses graininess and noise and enhances sharpness, can be processed on digitized image data by using a computer or a dedicated processing device 14.

The image to be processed in the image processing method and apparatus of the present invention is not particularly limited, but may be a photographic image using a silver halide color photographic photosensitive material such as a silver halide color film, a photographic image by a digital camera, Not only hard copy images of printing and various copying machines, but also soft copy images displayed on a display device such as a television, a CRT of a computer, and a liquid crystal may be used. In particular, unlike the light-sensitive materials described in Examples 1 to 5 of European Patent No. 800,114A, there is no need to perform a desilvering treatment, and the light-sensitive material in which undeveloped silver halide or developed silver remains remains. Preferably, the image is carried. In the above explanation,
Although graininess is described as a typical example of noise to be suppressed in these images, the present invention is not limited to this, and noise inherent to the original image due to graininess of the photographic photosensitive material or the like, or these original image Noise that is added when digitally scanned by an image input device such as a scanner, or noise that is mixed in when digitally imaged by shooting with a video camera, electronic still camera, or digital still camera. Any noise can be used as long as the noise is a target to be suppressed. In the above description, black and white particles and pigment particles, which are composed of silver particles such as undeveloped silver halide particles and developed silver particles of a silver halide color photographic light-sensitive material, are used as the particles that are noises to be identified and separated and suppressed. However, the present invention is not limited to this, and R,
In addition to the random noises of G and B, for example, image input devices, specifically, fixed pattern noise such as image pickup devices (CCD or MOS type) such as scanners and digital cameras and image pickup devices, and moire caused by aliasing, etc. R,
Noise or artifacts having G or B color correlation or strong color correlation may be used.

Next, each step of the image processing method of the present invention will be described in detail. 1) First, the sharpness enhancement step will be described. Here, as a method of enhancing the sharpness of the image,
Unsharp masking (USM) or Laplacian are well known. Also in the present invention, by using these, the sharpness of the image can be enhanced if the sharpness degradation of the image is slight. Unsharp mask, the original image I ₀ (x, y) as follows from, I ₀ (x, y) averaged or blurred images <I ₀ (x, y)> edges found by subtracting the The emphasized component I ₀ (x, y) − <I ₀ (x, y)> is multiplied by the coefficient a to obtain the original image I ₀ (x, y).
y), the sharpness enhanced image I _S
This is a method to obtain (x, y). I _S (x, y) = I ₀ (x, y) + a [I ₀ (x, y) − <I ₀ (x, y)>] (1) where a adjusts the degree of sharpness enhancement. X and y are constants, and indicate the position of the pixel of interest in the image.

The Laplacian image I ₀ (x, y) of the second derivative (Laplacian) ▽ ² I ₀ (x, y) by subtracting from the original image, in a sharpness emphasizing process is represented by the following formula . I _S (x, y) = I ₀ (x, y) − ▽ ² I ₀ (x, y) (2) As a specific example of sharpness enhancement by Laplacian, a 3 × 3 coefficient array as follows: Is often used. 0 -1 0 -1 -1 -1 1 -2 1 -1 5-1 -1 9-1 -1 -2 5 -2 (3) 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 ] (4) where σ ² is a parameter representing the spread of the normal distribution function, and 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] (5) 0.1 By adjusting to be ~ 1.0,
The sharpness of the 3 × 3 unsharp mask can be made desired. If the value of the above equation (5) is set to a value close to 1.0, it is possible to produce a mask substantially the same as the central Laplacian filter of the equation (3). 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 of 5 × 5, 7 × 7, 9 × 9, etc., the spatial frequency range of sharpness enhancement is used. Can be changed significantly.

Further, as the function form of the mask, a mask other than the above normal distribution type, for example, an exponential function type mask as shown in the following equation (6) can be used. E (x, y) = exp [− (x ² + y ² ) ^1/2 / a] (6) where a is a parameter representing the spread of the unsharp mask as in σ ^{2 in the} above equation (4). Where E (x ₁ , 0) / E (0,0) = exp [−x ₁ / a] (7) where the ratio of the edge value of the mask to the center value of the mask is 0.1 to 1.0. By adjusting so that
The sharpness of the 3 × 3 unsharp mask can be made desired. In equation (8), E (x ₁ , 0) / E (0,0) =
A numerical example of a mask of the exponential function of Expression (6) when 0.3 is set is shown. 0.18 0.30 0.18 0.30 1.00 0.30 (8) 0.18 0.30 0.18 When one example of an unsharp mask is calculated from this mask, the following equation (9) is obtained. −0.12 −0.22 −0.12 −0.21 2.32 −0.21 (9) −0.12 −0.21 −0.12

Using such an unsharp mask,
The sharpness enhanced image I _S (x, y) can be obtained from the original image I ₀ (x, y). The unsharp mask and the sharpness enhancement method used in the present invention are not limited to those described above, and other conventionally known unsharp masks and sharpness enhancement methods using spatial frequency filtering or the like can be applied. Of course.

2) Next, the smoothing step 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, the following formula (10) can be given as an averaging method using a weight coefficient.

[0039]

(Equation 1)

Where n is the mask size for averaging. In the present invention, in the real space area processing, a method of obtaining an average value by multiplying by a normal distribution type weighting coefficient 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 × 9
It is preferable to use those having a degree. w ₁₁ w ₁₂ w ₁₃ ··· w _1n w ₂₁ w ₂₂ w ₂₃ ··· w _2n w ₃₁ w ₃₂ w ₃₃ ··· w _3n · · · · (11) · · · · w _n1 w _n2 w _n3 ... w _nn

Equation (12) shows an example of a mask of 9 × 9 pixels. In this equation (12), 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 (12) 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 original image I ₀ (x,
From y), a smoothed image I _L (x, y) can be obtained. 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.

3) Next, a description will be given of a step of extracting a mixed component of granularity and edge. The difference between the sharpness-enhanced image I _S (x, y) and the smoothed image I _L (x, y) thus obtained, that is, the following equation (1)
3) is calculated, and the mixed component ΔI _EG (x, y) of the granularity and the edge is calculated.
Extract as _{ΔI EG (x, y) =} I S (x, y) -I L (x, y) (13)

4) The edge detection step 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.

Preprocessing: When performing density conversion and edge detection, first, in order to reduce graininess and noise without correlation in R, G, and B, and to improve edge detection accuracy, the original image I ₀ (x, y) Density value D of three colors of R, G, B
_R , D _G , and D _B are converted to a visual density D _V. The conversion formula is R, G, B as shown in Expression (14).
Three color density values D _R, D _G, weighting coefficient r in D _B, g, b
The over visual density is to convert (Visual density) D _V. The _{D V = (rD R + gD} G + bD B) / (r + g + b) (14) weighting coefficients, for example, r: g: b = 4 : 5: using a value such as 1. This conversion is performed to reduce graininess and noise that have no correlation between R, G, and B, and to improve the accuracy of edge detection. The size of the array of the pre-processing is preferably 5 × 5 or 7 × 7 pixels, but it is possible to reduce the fluctuation of the image density in the array in the next process by using a small array in the array, For example, the calculation is performed while moving using an array of about 3 × 3.

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 r: g: b = 2: 7: 1 Here, the preferable range of the coefficient ratio r: g: b is as follows. Assuming that r + g + b = 10.0, 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.

[0047] Edge detection by local variance, edge detection is, N from the image data of the visual density D _V
By moving the array of _E × _NE pixels and calculating the local standard deviation σ at each position as a local variance by using the equation (15), the image density fluctuation in the array is calculated. An object edge is detected. Pixel array size (N
_E × N _E ) may be appropriately determined in consideration of detection accuracy and calculation load. For example, it is preferable to use a size of about 3 × 3 or about 5 × 5.

[0048]

(Equation 2) Here, D _ij is the density of the N _E × N _E pixel array for calculating the local variance, and <D> is the average density of the array, which is represented by the following equation (16).

[0049]

(Equation 3)

5) Next, a description will be given of a process of calculating a weighting coefficient for a granular area and an edge area by edge detection. The local variance σ (x, y) shown in the above equations (15) and (16)
, A non-linear conversion (look-up table (LUT) conversion) is performed as in the following equations (17) and (18).
Weighting coefficient (data) W for edge area and granular area
_E (x, y) and W _G (x, y) is determined. Where σ (x, y)
= Σ _ij . _{W E (x, y) =} L {σ (x, y)} (17) W G (x, y) = 1-W E (x, y) (18) Here, shown in the equation (17) LUT transformation L ｛σ (x, y)
｝ Can be represented by the following equation (19). L {σ (x, y)} = 1−exp [−σ (x, y) / a _E ] (19) where a _E is the weighting of the value of the local variance σ (x, y) W
_E (x, y) in the coefficient used to convert to, assigned to _{W E = 0.5 σ (x,} y) when the threshold sigma _T of a _{a E = -σ T / log e} (0.5). The value of σ _T needs to be an appropriate value depending on the size of the signal of the granularity and the contour of the subject.
In a (256 gradation) color image, a value in the range of 10 to 100 is preferable. It should be noted that the non-linear conversion is performed by using the LUT
(Look up table) is preferable because the calculation time required for conversion can be reduced.

The nonlinear conversion equation L {σ (x, y)} for obtaining the weight W _E (x, y) of the edge region is not limited to the above equation, and other equations may be used. it can. For example, a Gaussian function such as the following equation (20) may be used. L ｛σ (x, y)｝ = 1−exp ｛− [σ (x, y)] ² / a _E1 ² ｝ (20) where a _E1 is derived from σ (x, y) to W _E (x, y) )), The threshold of σ (x, y) assigned to W _E = 0.5
When is _T, is ^{_{a E1 2 = -σ T 2 /}} log e (0.5). 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. In the above-described edge detection method using the local variance method, the local variance σ (x, y) is calculated, and the weighting coefficient W _E of the edge region is directly obtained by nonlinear conversion. However, the present invention is not limited to this. Instead, the weight coefficient W _E of the edge region may be obtained from the obtained local variance σ (x, y).

In the present invention, the edge detection method 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 represented by the following equation (21). g (i, j) = {[f (i, j)-f (i + l, j + l)] ² + [f (i + l, j)-f (i, j + l)] ² ｝ ^1/2 (21) Robinson using a 3 × 3 template corresponding to an edge pattern in eight directions as a template type operator
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.

Next, the weighting of the grain and the edge in the step of identifying and separating the grain and the edge will be described. Here, in order to identify and separate the granular component and the edge component, characteristics of the granularity and the edge are used. First, in the spatial region, the granularity is present in the entire film, that is, in the entire image, but is more noticeable in a portion that is flatter than the contour or edge portion of the subject.
On the other hand, the edges are mainly in the contour portion of the subject and the portion having the fine structure on the subject surface in the image. In the density region, the granularity is mainly constituted by the granularity of the photographic material used for photographing, so that the density difference is often small,
The edge depends on the contrast of the subject and greatly differs depending on the image, but the density difference varies from a very small to a very large one.

6) Next, the step of discriminating / separating the granularity and the edge will be described. In order to discriminate and separate the granularity and the edge, first, using the spatial characteristics of both, the region is divided into the granularity and the edge.
Multiplying the weighting factor of the granular region obtained by using the edge of the subject detected from the original image W _G (x, y), the mixed image data of the granular and the edge (density change values) ΔI _EG (x, y) Accordingly, the edge information is reduced in the edge region, and the granular component G ₀ (x, y) having a high ratio of the granular information in the granular region
Can be obtained. G ₀ (x, y) = W _G (x, y) × ΔI _EG (x, y) (22)

In the present invention, the granularity and edge image information may be separated by utilizing the characteristics in the density region. That is, a signal with a small density difference ΔD (x, y) is mainly a granular component, and some edge signals are mixed. A signal with a large density difference is mainly an edge component, and a granular component with a large density difference is mixed. Therefore, the granularity and the edge can be separated using the magnitude of the density difference. Granular component G
Separation of ₀ (x, y) can be performed using a non-linear conversion LUT expressed by the following equation (23). _{G 0 (x, y) =} LUT (ΔD (x, y)) (23) However, LUT is LUT (ΔD) = ΔD × exp - in _{^{[(ΔD) 2 / a G}} 2] (24), a G ² is a constant determined from the threshold G _T of density variation of particulate, is ^{_{a G 2 = -G T 2 /}} log e (1/2).

Here, the threshold value G _T of the granular density fluctuation is such that in the mixed image data ΔI _EG (x, y) of the granular and the edge, the density fluctuation smaller than this value is regarded as the granular. There is
As can be easily understood from the equation (24), on
The granularity to be separated is reduced in accordance with the LUT shape that becomes smaller as the density fluctuation increases, instead of separating the particles in / off. Therefore, the edge is mixed together with the granularity, but the ratio gradually decreases. Such a nonlinear conversion LUT is converted to a nonlinear conversion function NL.
G, and by referring to the above equations (23) and (24), the granular component G ₀ (x, y) is calculated by the above equation (22).
May be expressed as the following equation (25). G ₀ (x, y) = NLG ｛ΔI _EG (x, y) × W _G (x, y)｝ (25)

Incidentally, it is preferable to select an optimum value of the granularity discrimination threshold G _T depending on the granularity of the image to be processed, the size of noise, and the degree of sharpness enhancement processing. Since the granularity is identified in the image that has been subjected to the sharpness enhancement processing, the granularity is a granularity in which the original image has been sharpened by the sharpness enhancement processing and the density fluctuation has increased. Therefore, when performing the graininess suppression process,
Referring to the concentration variation of n × n pixels of interest represents the magnitude of the particulate after sharpness enhancement in the physical values such as RMS granularity sigma, it will determine the identification threshold G _T granular accordingly. Hereinafter, the determination method will be described.

The granularity of the color photographic light-sensitive material is usually measured by RMS granularity using a microdensitometer with a measuring aperture of 48 μφ, and is generally or a typical color negative film, for example, Super GACE. 100, 2
In the case of 00, 400, 800 (all manufactured by Fuji Photo Film Co., Ltd.) and the like, the values are 4 to 5 (expressed by multiplying the RMS granularity σ ₄₈ by 1000). When this film is digitized by scanning with the opening area A, the film granularity σ _sc at the opening area can be calculated by using the well-known Selwyn granularity equation S = σ√A.
From the RMS granularity σ ₄₈ measured at the opening of 8 μφ, the following equation (2)
It can be converted by 6). σ _sc = σ ₄₈ √A ₄₈ / √A _sc (26) where A ₄₈ is the area of the opening of ₄₈ μφ. For example, if the granularity of the film is 4 and the scanning aperture for digitization is ₁₂ μφ (the aperture area is A ₁₂ ), then σ _sc = σ ₄₈ √A ₄₈ / √A ₁₂ = 0.016 (27). However, in any case, the blur caused by the optical system and the scanning aperture is assumed to be the same.

The granularity σ when sharpness enhancement is performed
_{If sc} becomes p times larger, σ _sc ′ = pσ _sc (28). The value of the granularity discrimination threshold G _T is preferably a value proportional to the granularity of the image to be processed, that is, G _T = k _G σ _sc ′. However, k _G is a constant, and a value of 1.0 to 3.0 is preferable. Although more granular that can be more fully identified to be larger than the value of G _T sigma, increases the probability that the subject information of the low-contrast closer to density variations of the particulate is mistaken as a particulate. Conversely, if the value is smaller than σ, the subject information is less likely to be erroneously recognized, but the granularity having a large density variation cannot be caught in the granularity, and coarse granularity remains in the image.

7) The step of identifying and separating black and white (silver) granular components and pigment granular components will be described. Black-and-white granular components formed by silver halide or developed silver are spatially finer than pigmented granules, and in particular, the developed silver is opaque, so that the density fluctuation is large. Further, the black-and-white granular components due to silver halide or developed silver have a substantially flat spectral density distribution of the silver image, and thus are included as common components in the image / granular signals of each color of R, G, and B scanned by the scanner. . On the other hand, the pigment particulate component is translucent and consists of a pigment cloud formed by spreading widely around the developed silver particles.The density of the pigment cloud is high at the center and the density decreases at the periphery. Is larger spatially than the black-and-white granular component, but the density fluctuation is generally smaller than that of the black-and-white granular component. In the present invention, utilizing such characteristics of both, the spatial magnitude of the density fluctuation and the magnitude of the fluctuation are quantified, and based on the numerical values, the black-and-white granular component and the pigment granular component due to the silver granularity are distinguished. Can be.

Therefore, in the present invention, there is no particular limitation on a method of numerically expressing the spatial magnitude of the density fluctuation and the magnitude of the fluctuation, but an n.times.n pixel array (p, y) centered on the pixel (x, y). However, the local granularity coefficient G _L (x, y) as shown in the following equation (29) is preferably obtained from (n is about 3, 5, 7). G _L (x, y) = [S ₁ (x, y) + S ₂ (x, y)] / 2 (29) where S ₁ (x, y) and S ₂ (x, y) are It is a numerical value that characterizes the density fluctuation due to local granularity represented by the following equations (30) and (31).

[0062]

(Equation 4)

S ₁ (x, y) represented by the above equation (30) is n
× 2 represents the magnitude of the granular density fluctuation from the average value of the pixels, and S ₂ (x, y) expressed by the above equation (31) represents the density gradient between the central pixel and the peripheral pixels. In any case, the value becomes large when spatially minute density fluctuations such as black and white granular components are large, and becomes small when density fluctuations are gentle such as pigment granular components. Therefore, where the value of the local granularity coefficient G _L (x, y) is large, the black-and-white granular component is large, and where the value of G _L (x, y) is small, the pigment granularity is large. Can be separated.

It should be noted that the local granularity coefficient G _L (x, y) represented by the above equation (29) is further given a density gradient S ₃ (x, y) between neighboring pixels as shown in the following equation (32). May be added. S ₃ (x, y) = ｛(G ₀ (x, y + 1) −G ₀ (x-1, y + 1)) ² + (G ₀ (x + 1, y + 1) −G ₀ ( ^{x, y + 1)) 2} + (G 0 (x + 1, y) -G 0 (x + 1, y + 1)) 2 + (G 0 (x + 1, y-1) -G 0 ( ^{x + 1, y)) 2} + (G 0 (x, y-1) -G 0 (x + 1, y-1)) 2 (32) + (G 0 (x-1, y-1) - G ₀ (x, y-1)) ² + (G ₀ (x-1, y) −G ₀ (x-1, y-1)) ² + (G ₀ (x-1, y + 1) − G ₀ (x-1, y)) ² ｝ ^{1/2/8 Then} , the above equation (29) becomes the following equation (33). G _L (x, y) = [S ₁ (x, y) + S ₂ (x, y) + S ₃ (x, y)] / 3 (33)

The local granularity coefficient G thus obtained_L(x,
y) and the previously determined granular component G ₀From (x, y), black
White granular component G_Ag(x, y) and pigment granular component G_Dye(x, y)
Calculated by the following equations (34) and (35), respectively
be able to. G_Ag(x, y) = G₀(x, y) × G_L(x, y) / G_Lmax (34) G_Dye(x, y) = G₀(x, y) −G_Ag(x, y) (35) where G_LmaxIs the G of the entire image_LMaximum value of (x, y)
Or a constant multiple of the average value (for example, 2
Times), and silver halide grains contained in the image
Amount of particles and developed silver, graininess of original image, and sharpness
The value may be set based on the strength of the loudness enhancement.
If the image data is 8 bits for each color of R, G, B,
A value falling within the range of 10 to 100 is preferred.

8) The process of calculating the black and white granular and pigment granular inhibitory components will be described. The black-and-white granular component G _Ag (x,
y) multiplied by the suppression constant α _Ag to obtain the black-and-white granular suppression component G
_Ag (x, y) ′, and the product obtained by multiplying the pigment particle component G _Dye (x, y) obtained by the above equation (35) by the suppression constant α _Dye is the pigment particle suppression component G _Dye (x, y). ) '. G _Ag (x, y) '= α _Ag × G _Ag (x, y) G _Dye (x, y)' = α _Dye × G _Dye (x, y) (36)

9) Finally, a description will be given of the graininess suppression step from the sharpness-enhanced image (the step of creating a final processed image of graininess suppression / sharpness enhancement). From the sharpness enhanced image I _S (x, y) data obtained by equation (1) in the sharpness enhancement step, a black-and-white granular suppression component G _Ag (x, y) ′ and a pigment granular suppression component G _Dye (x, y) ′ are obtained. subtracting, by selectively removing the black-and-white and dye granularity suppression component granular region of sharpness enhanced image I _S,
A sharpness-enhanced image with suppressed graininess can be obtained. I ₁ (x, y) = I _S (x, y) −G _Ag (x, y) ′ − G _Dye (x, y) ′ (37) In this way, the original image I ₀ (x, y) is granular or the like. Is obtained, and the final processed image I ₁ (x, y) in which sharpness is sufficiently enhanced can be obtained. The image processing method of the present invention and the image processing apparatus for implementing the method are basically configured as described above.

The image processing method and apparatus for suppressing noise and enhancing sharpness of a digital image according to the present invention have been specifically implemented for various original images. First, when the photosensitive material described in Examples 1 to 5 of European Patent No. 800,114A is used to form an image by the image forming method described in the example, the image processing is performed as shown in FIG.
And the image processing apparatus of the present invention shown in FIG.
When the image processing method for suppressing graininess and enhancing sharpness according to the present invention described in (1) was carried out, an image in which graininess was similarly suppressed and sharpness was significantly improved was obtained.

The image processing method of the present invention is applied to a conventional main silver salt color photographic light-sensitive material having no undeveloped silver halide particles or developed silver particles, that is, a photographic image taken on a color negative film and a color reversal film. 3
When applied to 5 mm, brownie, new photo system APS, film with lens (LF), instant, etc., it was possible to obtain a remarkable improvement in both graininess and sharpness at a glance. In particular, the graininess has a processing effect comparable to the improvement of graininess by making the silver halide color photographic light-sensitive material finer, so that "blurred graininess" was a drawback of various grain removal processing methods based on conventional averaging and reduction of fluctuation.
No unnaturalness or discomfort. Also, with respect to sharpness, by combining with the above-described graininess suppression, a considerably larger emphasis effect can be obtained than with a conventional unsharp mask or Laplacian filter.

In the above-described example, a flat region identified and separated from an edge region from a sharpness-enhanced image is regarded as a granular region, and a granular component existing in this flat region is regarded as a silver halide particle or a developed silver particle. While the black and white granular components composed of silver particles and the pigment granular components such as pigment clouds are distinguished and separated, these are selectively suppressed,
In the edge region, noise suppression such as granular suppression is not performed, but the present invention is not limited to this. In the edge region, the granular component superimposed on the image edge is identified as a black-and-white granular component and a pigment granular component. -These may be separated and selectively suppressed. However, unlike the flat region, the edge image in the edge region has a strong color correlation,
Since it is difficult to distinguish the difference in morphological characteristics between black-and-white particles and pigment particles, in the edge region, black-and-white particles due to undeveloped silver halide particles or silver particles composed of developed silver particles and pigment particles due to pigment clouds are used. Difficult to identify.

In the image of the silver halide color photographic light-sensitive material, the silver edge image in the edge region also forms a grain at the same time, but since the image is mainly formed together with the dye edge image, the silver grain is more suppressed. Often there is no need to do so. Rather, the silver edge image in the edge region may exhibit an effect of making the image edges and contours stand out like a black plate of a printed image, and it may be better to actively leave it. In addition, if the silver edge image of the edge region is left without being suppressed, if the original image has an edge or contour of a color object, a black edge or outline remains on the edge or contour of the color object, which is preferable as an image. Will be gone. For this reason, in the edge region, it is generally preferable that the black-and-white image due to the undeveloped silver halide particles and the developed silver particles is not removed and is left to improve the image quality. Accordingly, it is preferable to appropriately determine whether to remove or leave a black and white image due to undeveloped silver halide grains or developed silver grains.

In the above description, in an image carried on a silver halide color photographic light-sensitive material, in particular, a light-sensitive material described in European Patent No. 800,114A, undeveloped silver halide particles are used together with developed silver particles. However, the present invention is not limited to this. For example, as in the case of processing using a dry fixing member described in JP-A-8-89977, a transparent material using a silver halide solvent is used. To reduce the contribution of the undeveloped silver halide grains to the black-and-white granular components. In this case,
Most of the black and white granular components are constituted by developed silver particles. Needless to say, in the present invention, the transparentizing treatment of the remaining undeveloped silver halide may or may not be performed.

Although the image processing method and apparatus for suppressing noise and enhancing sharpness of a digital image according to the present invention have been described in detail with reference to embodiments, the present invention is not limited to these embodiments and departs from the gist of the present invention. Of course, various improvements and design changes may be made without departing from the scope.

[0074]

As described in detail above, according to the image processing method and apparatus of the present invention, the granular component as well as the subject component of the image are refined by sharpness enhancement, and the granular component is converted from the sharpness enhanced image. Since the graininess is suppressed by a subtraction method, it is possible to realize a graininess that is spatially finer than the original graininess and has a small change in shading.
Since the grains are spatially refined, fine grains can be obtained, for example, when a fine grain emulsion is used in a silver halide color photographic light-sensitive material.

Further, when the present invention is applied to a digitized image of an image photographed on a silver halide color photographic light-sensitive material, particularly a light-sensitive material containing undeveloped silver halide grains and developed silver grains in addition to dyes, Defects such as those seen in conventional image processing methods, namely, that graininess is emphasized and visually uncomfortable, image signals with low contrast are misidentified as grainy, and are suppressed or removed, grain removal areas and An image in which graininess is suppressed and image sharpness is greatly enhanced can be obtained without causing a defect that boundaries of sharpness enhancement regions are discontinuous and unnatural artifacts are seen in the image. Further, according to the present invention, noise such as graininess is emphasized sharpness and spatially refined, so that silver halide color photographic light-sensitive materials have fine graininess as obtained when a fine grain emulsion is used, and smoothing is performed. A natural graininess suppressing effect without visual discomfort or discomfort such as blurred graininess which is a drawback of the conventional method used can be obtained.

Further, when the present invention is applied to an image captured by a digital still camera or the like, R, G,
Noise and artifacts that have a B color correlation,
That is, noise and artifacts having R, G, and B color correlations such as fixed pattern noises of image pickup devices and image pickup devices (CCD and MOS type) and moire due to aliasing, and photon noises and electronic circuits. By distinguishing / separating from random noise such as thermal noise and removing / suppressing them, an improvement in image quality 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, a color photographic image is read, image processing of graininess suppression / sharpness enhancement is performed, and a color image is output by an output device. is there.

FIG. 2 is a block diagram showing an embodiment of an image processing apparatus for suppressing graininess and enhancing sharpness according to the present invention.

FIG. 3 is a flowchart illustrating an embodiment of an image processing method for enhancing sharpness while suppressing graininess according to 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 granularity suppression / sharpness enhanced image processing unit 22 image monitor / image processing parameter setting unit 24 sharpness enhancement processing unit 26 smoothing process Unit 28 edge / granular mixed component extracting unit 30 edge detecting unit 32 granular region weighting coefficient calculating unit 34 granular component identification processing unit 36 output image calculating unit

Claims (4)

[Claims] 1. An original image data is subjected to a sharpness enhancement process, and together with the image, sharpness-enhanced image data in which the granularity or noise contained in the image is sharpened is created, and the original image data is subjected to a smoothing process, The smoothed image data is created, the smoothed image data is subtracted from the sharpness-enhanced image data, and the edges of the subject image in which the sharpness-enhanced subject image edge and the sharpness-enhanced grain shape are mixed are mixed. Image data, and performing edge detection from the original image data to obtain edge area weighting data and granular area weighting data for identifying a subject edge area and a granular area. Is multiplied by the weighted data of the granular region to the mixed image data of Data is determined for each color, and from this granular data of each color, a local granularity coefficient representing the spatial magnitude of the density fluctuation due to the granularity and the characteristic of the magnitude of the fluctuation is determined, and the black and white granular component and the pigment granular component are identified and separated. Multiplying the obtained black-and-white granular component and the pigment granular component by the respective suppression coefficients to obtain a black-and-white granular suppressing component and a pigment granular suppressing component, and obtaining the black-and-white granular suppressing component and the pigment granular component from the sharpness emphasized image data. Image processing for noise suppression and sharpness enhancement of digital images, characterized by creating a processed image in which graininess is suppressed by selectively removing suppression components and sharpness enhancement in an image edge region is maintained. Method. 2. The original image data is digital image data recorded by an image recording device from an image photographed on a silver halide color photographic light-sensitive material, wherein the black-and-white particles are formed of undeveloped silver halide particles and developed silver particles. The image processing method according to claim 1, wherein the image processing method includes at least one of particles. 3. The original image data is digital image data recorded by an image recording device from an image photographed on a silver halide color photographic light-sensitive material, wherein the black and white particles are the undeveloped silver halide particles and the developed silver particles. 3. The image processing method according to claim 1, further comprising at least one of random noise of each color, fixed pattern noise of the image recording device, and moire due to aliasing, in addition to the granularity of at least one of the following. 4. A sharpness processing section for performing sharpness enhancement processing on the original image data to create sharpness-enhanced image data in which graininess or noise contained in the image is sharpened together with the image; and a smoothing processing on the original image data. A smoothing processing unit that creates smoothed image data, and subtracts the smoothed image data from the sharpness-enhanced image data to obtain a sharpness-enhanced subject image edge and a sharpness-enhanced grain. , An edge / granular mixed component extraction unit that creates mixed image data of the subject image edge and the granularity, and an edge region for identifying the subject edge region and the granular region by performing edge detection from the original image data. An edge detector for obtaining weighting data; and a weight of the granular region based on the weighting data of the edge region. A granular region weighting coefficient calculating unit for obtaining weighting data; and multiplying the mixed image data of the subject image edge and the granularity by the weighting data of the granular region to obtain granular data of the granular region for each color. From the data, the spatial granularity of the density fluctuation due to the granularity and the local granularity coefficient representing the characteristics of the magnitude of the fluctuation are determined, the black and white granular component and the pigment granular component are identified and separated, and the obtained black and white granular component and the pigment granular component are obtained. And a granular component discriminating processing unit for obtaining a black-and-white granular suppression component and a pigment granular suppression component by multiplying the respective suppression coefficients, and selectively selecting the black-white granular suppression component and the pigment granular suppression component from the sharpness-enhanced image data. And an output image calculation unit for creating a processed image in which graininess is suppressed and sharpness enhancement in an image edge region is retained. The image processing device for graininess suppression and sharpness enhancement of a digital image, characterized in that.