JP2001076134A - Picture processing method and picture processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To suppress granular shapes and to emphasize sharpness by sharpening an object picture and noise by emphasizing sharpness and extracting an object edge component and a noise component from the picture. SOLUTION: A sharpness emphasis processing is executed from original picture data I0 in a sharpness emphasis processing part 24, a picture is sharpened and noise with granular shapes contained in original picture data is sharpened. Then, an edge and noise are area-divided. A smoothing processing part 26 generates a smoothed picture from an original picture and operates them from a picture where sharpness is emphasized. Thus, the coexisting component of the edge and noise is obtained. In an edge area, sharpness is emphasized and noise is selectively suppressed in a noise area. A noise component where sharpness is emphasized is especially identified. When the amplitude of the noise component is large, non-linear amplitude suppressing amplitude is compressed/ converted in a case that amplitude is small.

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 image processing for suppressing digital image noise and enhancing sharpness, which suppress noise such as graininess of a digital image and enhance the sharpness of the digital image. Related to the device.

[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.

[0006] In this processing method, since the granular pattern is blurred, the contrast of density of the granular pattern is reduced, but a large uneven pattern made up of mottles of particles constituting the granular pattern is visually noticeable. Or it 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, fine images of grass and carpets,
This drawback appears in textures and in images depicting textures such as fabrics, which are 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. In other words, in such a conventional method, blurring and averaging are used as a method of suppressing graininess, and the blurred grain pattern becomes smaller as density fluctuation and looks as if graininess has improved. Small, but blurred and spread granular patterns are visually perceived as offensive patterns,
In particular, there has been a problem that a uniform subject such as a face or skin such as a portrait photograph or a wall or the sky stands out.

In a conventional method of separating a granular (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 an undecided state is applied to each region. By processing using different coefficients such as a sharp mask and Laplacian, the graininess is suppressed in the flat area, and the sharpness is enhanced in the outline area to suppress the graininess without blurring the edges. Since the recognition and separation of regions 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 enhancement or sharpness enhancement method.
There is a problem that a border such as a Mackey line is easily generated at the contour / edge portion of the image, giving a visually unnatural impression. The problem of suppressing noise such as graininess and the problem of sharpness enhancement are not problems unique to silver halide photography, and various problems such as shot noise and electrical noise are also observed when shooting with a digital camera or the like. This occurs as a problem of suppressing noise 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 erroneously recognized as noise and are suppressed or removed, and the boundary between the noise removal area and the sharpness enhancement area becomes discontinuous, causing IMAGE PROCESSING METHOD FOR DIGITAL IMAGE NOISE SUPPRESSION AND SHARPNESS ENHANCEMENT PERFORMING PROCESSES FOR REDUCING GRAIN AND IMPROVING IMAGE SHARPNESS WITHOUT PRODUCING PROBLEM OF REALIZING ARTIFACTS AND IMAGE PROCESSING IMPLEMENTING THE SAME It is intended to provide a device.

[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 obtain a sharpness-enhanced edge of the subject image and the same sharpness. Create mixed image data in which the emphasized noise is mixed, obtain edge intensity data for identifying a subject edge region and a noise region by performing edge detection from the original image data, and obtain an edge region of the edge region from the edge intensity data. A weighting coefficient and a weighting coefficient for the noise area are obtained, and the weight of the noise area is assigned to the mixed image data. Multiplying by a coefficient to obtain noise data in a noise region, and performing a compression conversion of compressing the noise data without inverting the magnitude relationship of the amplitude of the noise data to the noise data, and as the amplitude of the noise data increases, The compression rate is smoothly increased to perform amplitude non-linear compression conversion to generate noise amplitude suppression data.On the other hand, the mixed image data is multiplied by a weighting coefficient for the edge area to obtain edge enhancement data for the edge area. ,
By adding the noise amplitude suppression data and the edge emphasis data to the smoothed image data to obtain processed image data, a noise component in a noise region of the original image is selectively suppressed, and a sharpness in an edge region of the original image is obtained. And an image processing method characterized by creating a processed image emphasizing.

[0011] Here, the amplitude non-linear compression transformation, when the noise data and G _i, preferably represented by the following formula (1). _{L GC (G i) = G} max · {1- exp [- (log e 2) · G i / G T]} (1) where, G _T, the noise of adjusting the characteristics of the amplitude non-linear compression transformation _Gmax is a compression threshold, and _Gmax is an amplitude adjustment constant that specifies a compression amplitude level. At this time, it is preferable that the noise compression threshold is determined based on a standard deviation or an average value of the noise data. Preferably, the amplitude non-linear compression conversion is performed using conversion data of the amplitude non-linear compression conversion in which noise amplitude suppression data for noise data is obtained in advance as a look-up table.

Further, the present invention provides a sharpness enhancement processing section for performing sharpness enhancement processing on original image data to create sharpness enhanced image data in which noise included in the image is sharpened together with the image; Performing a smoothing process, a smoothing processing unit that creates smoothed image data, and an edge detection unit that performs edge detection from the original image data to obtain edge intensity data for identifying a subject edge region and a noise region. Subtracting the smoothed image data created by the smoothing processing unit from the sharpness enhanced image data created by the sharpness enhancing unit, and sharpening the edges of the subject image and noise that has also been sharpened. An edge / noise mixed component extraction unit for creating mixed image data in which An edge / noise weighting factor calculation unit for calculating a weighting factor for an edge region and a weighting factor for a noise region from the edge strength data obtained at the output unit; and the mixed image data created by the mixed edge / noise component extraction unit. Multiplying the noise area weighting coefficient calculated by the edge noise weight coefficient calculating section to obtain noise data of the noise area; further, the mixed image data is calculated by the edge noise weight coefficient calculating section. Multiplying the edge area weighting coefficient to obtain edge enhancement data of the edge area;
Separation unit, a compression conversion for compressing the amplitude of the noise data without reversing the magnitude relationship of the amplitude of the noise data, wherein the amplitude non-linearity in which the compression rate smoothly increases as the amplitude of the noise data increases A noise non-linear compression LUT calculation unit that obtains conversion data of the compression conversion in advance to create a look-up table; and the edge / noise component identification / separation unit uses the look-up table generated by the noise non-linear compression LUT calculation unit. A noise component compression processing unit for performing a non-linear compression process on the obtained noise data to obtain noise amplitude suppression data; and a noise component obtained by the noise component compression processing unit on the smoothed image data created by the smoothing processing unit. By adding the amplitude suppression data and the edge emphasis data obtained by the edge / noise component identification / separation unit, An output image calculation unit that outputs the physical image data, wherein noise image components in a noise region of the original image are selectively suppressed, and processed image data in which sharpness in an edge region of the original image is enhanced is created. An image processing device is provided.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an image processing method and an image processing apparatus according to the present invention will be described in detail based on preferred embodiments shown in the accompanying drawings.

FIG. 1 is a block diagram of a color image reproducing system that 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, a color photographic image will be described as a representative example as a digital image.

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)
A film scanner that reads color film images such as reversal film to create digital image data, a scanner device that scans color reflective original images such as printed matter and reflection print images to create digital image data, and a scanner that directly scans subjects Digital camera, electronic still camera, video camera, or a recording medium storing digital image data created by them, such as smart media, semiconductor memory such as PC card, FD, Zip, etc. Driver for reading magnetic image recording media such as MO and MD, and optical recording media such as CD-ROM, Photo-CD, etc., and reading them as digital image data. Show CRT monitor, can be cited a display device such as a liquid crystal monitor, and read or display digital image data wholly or partially the image processing to the image processing PC, and the like computer such as WS that.

The image output device 16 outputs 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 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 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, which includes parameters of various image processing performed by the color / tone processing unit 18 and the noise suppression / sharpness enhanced image processing unit 20 for implementing the image processing method of the present invention. It is for setting by. Here, parameters to be set include correction coefficients, conversion coefficients, magnifications, and the like used in the above-described various processes, and coefficients and constants necessary for implementing the image processing method of the present invention described in detail later. And the like.

A noise suppression / sharpness-enhanced image processing unit (hereinafter simply referred to as “main image processing unit”) 20 that implements the image processing method of the present invention includes a main image data I _O created by the color / tone processing unit 18. This is for creating processed image data I ₁ , which is final processed image data to be output to the image output device 16, by performing the noise suppression / sharpness enhanced image processing which is a feature of the present invention.

Here, the image processing section 20 is shown in FIG.
Thus, the original image data I_OSharpness enhancement processing
And the granularity contained in this image along with the image
Sharpness enhancement that sharpens noise (noise)
Image data I_SSharpness enhancement processing unit 24 for creating
And the original image data I_OTo the smoothed image.
Image data I_AVAnd a smoothing processing unit 26 for generating
Nest emphasized image data I _SFrom the smoothed image data I_AVReduced
The edge of the subject image with sharpness enhanced
Subject image edge mixed with noise Mixed with noise
Image data ΔI _EGEdge / noise mixed component extraction
Output unit 28 and original image data I_OFrom the edge of the subject image
Performs detection to identify subject edge areas and noise areas
Strength data E for_OEdge detector that calculates
30 and the edge strength data E _OTo edge area weight
Finding coefficient W_EAnd noise area weighting coefficient W_GAsk for
Edge noise weighting coefficient calculation unit 32, and edge noise
Image data ΔI obtained by the mixed component extraction unit 28_EG
Is the noise calculated by the edge / noise weighting coefficient calculator 32.
Weighting factor W_GMultiplied by
Data G₀, And the mixed image data ΔI
_EGThe edge noise weight coefficient calculation unit 32 calculates
Edge area weighting coefficient W_EMultiplied by
Edge emphasis data E₁Edge and noise component identification
Separation unit 34 and edge / noise component identification / separation unit 34
Noise data G obtained by₀Compression to compress the amplitude of
In other words, the noise data G₀As the amplitude of
Of the non-linear compression conversion, which increases the compression ratio smoothly.
Look up conversion data in advance and look up table L_GCMake
The noise non-linear compression LUT calculation unit 36 that generates
The lookup table created by the linear compression LUT operation unit 36
Table L_GCEdge / noise component identification / separation unit 3 using
Noise data G obtained in 4₀Perform nonlinear compression processing of
Noise amplitude suppression data G₁Noise component compression
Processing unit 38 and the smoothed image created by the smoothing processing unit 26
Data I_AVThe noise component compression processing unit 38
Noise amplitude suppression data G₁And edge noise component identification
The edge emphasis data E obtained by the separation unit 34₁Add
Processed image data I₁And the processed image data I₁
And an output image calculation unit 40 that outputs

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 operation of the image processing method and the image processing method of the present invention will be outlined.

In the present invention, as shown in FIGS. 2 and 3, for each pixel, a sharpness enhancement process is first performed on the original image data I _{O in} the sharpness enhancement processing section 24 (step 100). The data _IS is obtained, a smoothing process is performed in the smoothing processing unit 26 (step 102), the smoothed image data I _AV is obtained, and the edge and noise mixed noise is mixed in the edge / noise mixed component extraction unit 28. The extracted edge / noise mixed component is extracted (step 104), and the edge / noise mixed image data ΔI _EG is extracted.

On the other hand, the edge detection unit 30 obtains edge strength data E _O for discriminating a subject edge area and a noise area from the original image data I _O and performs edge detection (step 106). the computing section 32, determined by calculating a weighting coefficient W _G of the weighting factors W _E and the noise region of the edge region (step 1
08). Further, the edge / noise component identification / separation unit 34
In step (1), edge noise is identified and separated (step 110). That is, the mixed image data [Delta] I _EG, multiplied by the weighting coefficient W _E of the edge region determined by the edge noise weighting factor calculation unit 32, an edge enhancement data E ₁ of the edge regions determined, the mixed image data [Delta] I _EG, Edge
Multiplied by the weighting coefficient W _G of the noise region determined by the noise weighting factor calculation unit 32, noise data G of the noise region
_{Find 0} .

Next, the noise non-linear compression LUT calculation unit 36
In, a compression conversion for compressing the amplitude of the noise data G ₀ obtained by the edge noise component identifying and separating portion 34, without the amplitude magnitude of the noise data G ₀ is reversed, the noise data G ₀ The conversion data of the amplitude non-linear compression conversion in which the compression ratio smoothly increases as the amplitude increases is created as a look-up table _LGC (step 112). In this case, it is preferable that the amplitude non-linear compression conversion data is determined using a standard deviation σ _G (x, y) of noise data expressed by the following equation (29). Using the created look-up table L _GC , the noise data G _{0 in} the noise area is subjected to a non-linear compression process of the noise component (step 114), and the noise amplitude suppression data G ₁ is obtained. Finally, the noise component compression processing unit 38 is added to the smoothed image data I _AV created by the smoothing processing unit 26.
In the noise amplitude suppression data G ₁ obtained by performing noise suppression, sharpness enhancement operation for adding the edge enhancement data E ₁ obtained at the edge noise component identifying and separating unit 34 (step 116), processing the image data get the I _1.

That is, the present invention provides: a sharpness enhancement process for original image data to sharpen the image and sharpen both noises and other noises included in the original image data; An edge in the image is detected from the original image, an edge strength is obtained, a region having a large edge strength is regarded as a subject edge region having a large amount of subject contour information, and a region having a weak edge strength is determined as follows.
A region is divided assuming a noise region, and a smoothed image is created from the original image, and a mixed component of edges and noise is obtained by calculating from the sharpness enhanced image. Then, the noise is selectively suppressed. In particular, a noise component in which sharpness is emphasized is identified, and a non-linear amplitude compression conversion for suppressing the amplitude more strongly when the amplitude of the noise component is large than when the amplitude is small is performed. Therefore, in the edge region of the subject of the image, sharpness is enhanced, and in the noise region, a high-quality image in which noise is suppressed can be obtained. In particular, step 1
By the nonlinear compression processing of the noise component performed in step 12 and step 114, the noise component that has been subjected to sharpness emphasis is more strongly suppressed for the noise component having a large amplitude, and the noise component having a small amplitude is nonlinearly suppressed so that the noise component having a small amplitude is weakly suppressed. By performing a proper compression process, it is possible to reduce the noise density fluctuation. As described above, since the pattern formed by the noise component is emphasized with sharpness and the amplitude of the noise component is compressed, the fine granularity, that is, the spatially fine and the amplitude, which can be obtained when a fine grain emulsion is used in a silver halide photographic material, is used. Can also be reduced.

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. An unsharp mask is obtained by averaging or blurring I ₀ (x, y) from original image data I ₀ (x, y) (pixel positions of interest are assumed to be x and y) as shown in the following equation. I
₀ (x, y)> and the edge enhancement component I ₀ (x, y) − <I
₀ (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).
It is a method of seeking. I _S (x, y) = I ₀ (x, y) + a [I ₀ (x, y) − <I ₀ (x, y)>] (2) where a adjusts the degree of sharpness enhancement. Is a constant. 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) (3) 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 (4) 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 ] (5) 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] (6) 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 expression (6) is set to a value close to 1.0, a mask substantially the same as the central Laplacian filter of the expression (4) can be produced. 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.

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 (7) can be used. E (x, y) = exp [− (x ² + y ² ) ^1/2 / a] (7) Here, a is a parameter representing the spread of the unsharp mask as in σ ^{2 in} equation (5). And the ratio of the edge value of the mask to the center value of the mask, E (x ₁ , 0) / E (0,0) = exp [−x ₁ / a] (8): 0.1 to 1.0 By adjusting to become
The sharpness of the 3 × 3 unsharp mask can be made desired. In equation (9), E (x ₁ , 0) / E (0,0) =
A numerical example of the mask of the exponential function of Expression (7) when 0.3 is set is shown. 0.18 0.30 0.18 0.30 1.00 0.30 (9) 0.18 0.30 0.18 When this mask is used to calculate an example of an unsharp mask, the following equation (10) is obtained. −0.12 −0.22 −0.12 −0.21 2.32 −0.21 (10) −0.12 −0.21 −0.12

Using such an unsharp mask,
The sharpness-enhanced image data I _S (x, y) can be obtained from the original image data 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.

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 (11) is used.
Can be mentioned.

(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・ ・ ・ ・ (12) ・ ・ ・ ・w _n1 w _n2 w _n3 ... w _nn

Equation (13) shows an example of a mask of 9 × 9 pixels. In this equation (13), 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 (13) 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.

Next, the step of extracting edge / noise mixed components (step 104) will be described. Sharpness step (step 100) and smoothing step (step 1)
02) obtained in the sharpness enhanced image data I _S (x,
y) and the smoothed image data I _AV (x, y), the following equation (14)
, The edge / noise mixed component ΔI _EG (x, y), which is the fine structure data in which the noise and the edge in which the sharpness is emphasized are mixed, is extracted. ΔI _EG (x, y) = I _S (x, y) −I _AV (x, y) (14)

Next, an edge detection 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. Such pre-processing is performed by reducing noise components such as granularity having no correlation between the R image data, the G image data, and the B image data constituting the color image data, and reducing the accuracy in edge detection performed thereafter. It is for improving. That is, as shown in equation (15), the original image data I ₀ (x, y) R a, G, 3-color density values D _R of B, D _G, weighting coefficient r in D _B, g, b the over into a visual density (Visualdensity) D _V. The _{D V = (rD R + gD} G + bD B) / (r + g + b) (15) 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 set to a range of about 5 × 5 or about 7 × 7 pixels. 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 (16) 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
While moving the array of × n _E pixels, the image density variation in the array is calculated using Equation (17), 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.

[0041]

(Equation 2) However, let the target pixel position be x, y, and _DV (x + in / 2 -1 /
(2, y + jn / 2-1 / 2) is n _E × which calculates local variance σ (x, y).
at a concentration of pixel array of _{n E, <D V (x} , y)> is the average concentration 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 (17) is calculated to obtain the edge strength E _O (x, y) of the subject image. Is given by the following equation (1)
An expression expressed by an exponential function such as 9) is used. E _O (x, y) = 1−exp [−σ (x, y) / a _E ] (19) where a _E is a coefficient for converting the value of the local variance σ (x, y) into edge strength. Where 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) (20) 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.
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 ｝ (21) 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) (22). 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 shown in the following Expression (23). E ₀ (x, y) = σ (x, y) / σ _Max (23) 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 obtain the maximum value from the local variance data σ (x, y) of the entire image obtained by Expression (23) as in Expression (24) below. σ _Max = Max {σ (x, y)} (24)

Further, instead of using the maximum value from the entire image, a part of the image data, for example, a specific range of a central portion where an important subject of the image has a high probability, or image data thinned from the entire image (1 of the original image data). / 4 to 1/10)
May be used to determine the maximum value σ _Max using the above equations (23) and (24). 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 the top 5 when local variance data σ (x, y) is arranged in descending order.
Average of values located within 10%, for example the mean value of the values located within the upper 10% <σ (x, y)> a _{MAX 10%} and _{σ Max, σ (x, y} ) exceeds this sigma _Max Σ _Max
Replace with 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 distribution method, and other edge detection methods can also 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.

Next, calculation of the edge noise weighting coefficient
The process (step 108) will be described. Edge detection process
Edge strength data E obtained in (Step 106)
_OUsing (x, y), the edge area is calculated according to the following equation (25).
Area weighting factor W_EFind (x, y). W_E(x, y) = 1−α_E+ Α_EE₀(x, y) (25) Further, the weighting coefficient W of the noise region_G(x, y)
It is determined according to equation (26). W_G(x, y) = 1-W_E(x, y) (26) where α_ESets the weight of the edge region and the noise region.
This is a constant that is specified by the operator.
You can set a value. Edge area weighting factor
W_E(x, y) is 1-α_EAnd the value is 1 or less, and W
_EThe larger (x, y) is, the more
It is determined that the region is highly probable, and W_E(x, y) is small
The higher the probability of being a noise area at that pixel position.
Refused. Normalized edge strength data E₀(x, y) is
In an edge region having a value close to 1.0, α_EThe value of
The weight coefficient W of the edge region_E(x, y) increased to 1.0
A close large value, 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).

Next, the step of identifying and separating edge noise (step 110) will be described. Edge / noise mixed component ΔI _EG (x,
y), as shown in the following equations (27) and (28), an edge noise weighting coefficient calculating step (step 1)
08) weighting coefficient W _E (x, y) of the edge region obtained in step 08)
And the noise area weighting coefficient W _G (x, y), respectively, to obtain sharpened edge-enhanced data E _1.
(x, y) and noise data G ₀ (x, y) subjected to sharpness enhancement processing are obtained. _{E 1 (x, y) =} ΔI EG (x, y) × W E (x, y) (27) G 0 (x, y) = ΔI EG (x, y) × W G (x, y) ( 28)

Next, the noise compression LUT creation step (step 112) will be described. The noise compression LUT creation process includes an edge / noise identification / separation process (step 11).
A look-up table _LGC for nonlinearly suppressing the amplitude of the noise data G ₀ (x, y) subjected to the sharpness enhancement process obtained in step 0) is created in advance. That is,
The compression rate for compressing the amplitude of the noise data G ₀ (x, y) to which the sharpness enhancement processing has been performed and the noise is enhanced without reversing the magnitude relationship of the noise data G ₀ becomes smoother as the amplitude increases. The lookup table _LGC of the amplitude non-linear compression conversion is created according to the equation (1) so as to be larger. That is, when L _GC (G _i ) for G _i shown in the equation (1) is graphed, as shown in FIG. 4, the curve is monotonically increasing for G _i and converges to a constant value G _max. It can be represented by P. Where G
_i is an input value for obtaining noise amplitude suppression data G ₁ (x, y) whose amplitude is suppressed using the look-up table L _GC as described later, and the noise data G ₀ ( Since x, y) is an input value, a curve P obtained by graphing L _GC (G _i ) indicates the degree of compression of the amplitude of the noise data G ₀ . That is, the curve P in the case of small value close to G _i is 0, the slope passing through the origin is close to the constant linear Q, that is, the compression ratio by the conversion is substantially constant, the G _i increases, the curve P Gradually decreases, and the compression ratio by the conversion increases.

G _T is a lookup table L _GC
Is an amplitude nonlinear compression constant that defines the characteristics of nonlinear compression of
An appropriate value can be input according to the size of noise included in the image or the strength of sharpness enhancement. For example,
First, the fluctuation of the noise data G _{0 in} the entire original image area, that is, the noise standard deviation σ _G is obtained by the following equation (29).

(Equation 4)Where N_xAnd N_yIs the vertical and horizontal direction of the original image
The number of pixels in the direction _GIs determined by the following equation (30).
Weighting coefficient W of the entire noise region_G(x, y) original image area
It is the total sum of all.

(Equation 5)

Next, the noise standard deviation sigma _G obtained in equation (29), we obtain the noise reduction threshold G _T multiplied by a constant adjustment factor by the following equation (31). G _T = k _G · σ _G (31) Here, the adjustment coefficient k _G is preferably a value in the range of 0.5 to 2.0. Here, the noise standard deviation sigma _G seeking noise reduction threshold G _T, the total area of the original image, i.e. the standard deviation for all pixels need not necessarily be all the pixels, a portion of the image data of the original image , for example, the standard deviation of the noise data G ₀ specific area of the high probability of a principal object central portion of the image, about 1 / 4-1 / 10 of the image data of the original image obtained by thinning the image data of the original image using the standard deviation of the noise data G _0, it may be determined noise threshold of compression G _T from the formula (31).

[0052] Further, as a method of obtaining the noise reduction threshold G _T, in addition to the above methods, as represented by the following formula (32), the original image of the absolute value of the noise data G ₀ is the noise fluctuation component region of The average value <| G ₀ |> of all pixels is obtained,
This was used in place of the noise standard deviation sigma _G of formula (31) may be determined noise threshold of compression G _T.

(Equation 6) In this case, as in the case of calculating the noise standard deviation σ _G ,
Portion of the image data of the original image, for example, the average value and the noise data G ₀ for a specific region of the high middle part possibly a principal object of the image, the image data of the original image obtained by thinning the image data of the original image 1 The average value of the noise data G ₀ of about ４ to 1/10 may be used. By using such a noise compression threshold G _T , when G _i is equal to the value of the noise compression threshold G _T , the value of L _GC (G _i ) is half of G _max .

G _max shown in equation (1) is an amplitude adjustment constant for adjusting the compression level of the amplitude of the noise amplitude suppression data G ₁ (x, y), and can be set to a predetermined value.
For example, in the case of 8-bit color image data for each color of the original image data, a value of about 1 to 50 is preferable, and a value of 1 to 20 is more preferable. The amplitude compression constant _Gmax determines the magnitude of the noise of the processed image. When this value is reduced, the noise amplitude is small and the image is smooth, and when this value is increased, the noise becomes conspicuous.

Image processing method and image processing apparatus of the present invention
The amplitude non-linear compression conversion of the equation
Not only the conversion using the exponential function shown in (1) but also noise
Data G ₀The magnitude relation of the amplitude by the compression conversion of (x, y) is reversed
Noise data G without turning ₀Compress the amplitude of (x, y)
Compression ratio increases smoothly as the amplitude increases.
There is no particular limitation on the amplitude non-linear compression conversion.
No. As in the past, image processing allowed
Noise in natural form without creating natural boundaries
This is because suppression is achieved. That is, sharpness
The noise component whose amplitude has been emphasized and
Weighting coefficient W_Gselectively identified by (x, y)
Naturally compress large portions of noise with large amounts
Because you can do it.

Next, a noise component compression step (step 1)
14) will be described. Noise data G obtained in the edge / noise identification / separation step (step 110)
₀ (x, y), the lookup table L created in the noise compression LUT creation step (step 112) to nonlinearly compress the amplitude of the noise data G ₀ (x, y).
_{Using GC} (G _i ), the amplitude of the noise data G ₀ (x, y) is compressed according to the following equation (33) to obtain suppressed noise data G ₁ (x, y) with small fluctuation. G ₁ (x, y) = L _GC (G ₀ (x, y)) (33) If there is no corresponding input value in the look-up table of the non-linear conversion created in advance, a predetermined interpolation or the like is performed. To obtain the noise amplitude suppression data G _i (x, y).

As described above, the noise data G₀apply to (x, y)
Non-linear compression conversion compresses at a fixed compression rate
Compared to linear compression conversion, noise data is
TA G ₀Since the compression ratio changes according to the amplitude of (x, y),
In other words, the compression ratio is small and the amplitude is
As the compression ratio increases smoothly as the
The amplitude of fluctuations caused by noisy noise is reduced by linear compression
It can be made more uniform as compared with the replacement.

Finally, the noise suppression / sharpness enhancement calculation step (step 116) will be described. Smoothed image data I created in the smoothing step (step 102)
_As shown in the following equation (34), the noise amplitude suppression data G ₁ (x, y) obtained in the noise component compression step (step 114) is distinguished from _AV (x, y) by edge noise.・
The edge-emphasized data E ₁ (x, y) obtained in the separation step (step 110) is added to suppress the noise in the noise region, and the processed image data I ₁ (x, y) in which the sharpness is enhanced in the edge region. obtain. I ₁ (x, y) = I _AV (x, y) + E ₁ (x, y) + G ₁ (x, y) (34)

The image processing method and apparatus of the present invention can be applied to silver halide color photographs, for example, photographic images photographed on a 35 mm color negative film, in which edge areas are sharpened and noise areas are noise data. The compression ratio for compressing the noise data is increased without increasing the magnitude relationship of the amplitude, and the amplitude non-linear compression conversion is performed so that it increases smoothly as the amplitude increases.Therefore, the granularity is suppressed by smoothing and the granularity is blurred. Large irregularities do not appear visually uncomfortable, low-contrast image data is not mistakenly recognized as granular, and is not suppressed or removed.In addition, the boundary between the area to be removed and the area to be sharpened is discontinuous. At-a-glance clarity in noise and sharpness without unnatural artifacts in the image It can be obtained to improve the remarkable picture quality to an extent. In particular, with regard to the granularity, since it has a processing effect comparable to the improvement of the granularity in making the photosensitive material of a silver halide color photographic negative film into fine particles, it is a drawback of various types of granular removal processing based on the conventional averaging and reduction of fluctuation. It does not cause unnaturalness or discomfort such as "blurred grain". Further, with regard to sharpness, by combining with graininess suppression, a sharpness emphasis effect that is greater than that of a conventional unsharp mask or Laplacian filter can be obtained. Also, when applied to images taken with a digital still camera, similar to the above-described color film images, an image with emphasized edges is obtained while suppressing noise in a visually natural manner, and a remarkable improvement in image quality is obtained. Is obtained.

The image processing method and the image processing apparatus of the present invention have been described in detail above. However, the present invention is not limited to the above-described embodiments, and various improvements and modifications can be made without departing from the gist of the present invention. Of course you can do it.

[0060]

As described in detail above, according to the image processing method and the image processing apparatus of the present invention, first, the sharpness of the image is emphasized to sharpen both the subject image and the noise including the granularity. The noise is suppressed by extracting the subject edge component and the noise component from the image, and compressing the amplitude of the granular density fluctuation in the noise region nonlinearly, so that the noise is spatially finer than the original noise. It is possible to realize visually-natural noise suppression with small shading. Therefore,
According to the present invention, since sharpness is enhanced and spatially refined, the granularity in silver halide photography becomes fine granularity as obtained when a fine grain emulsion of a silver halide photographic material is used, and is smooth. And a natural graininess suppressing effect without visual discomfort or discomfort such as blurred graininess, which is a drawback of the conventional method using image formation. Further, by applying the image processing method of the present invention to a silver halide color photographic light-sensitive material, there was a drawback of the conventional granular suppression / sharpness enhancement processing method.
There is no so-called "blurred grain" unnaturalness or unnatural feeling, the graininess and sharpness are improved at the same time, 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 that incorporates an image processing device according to the present invention, reads a color photographic image, performs image processing for sharpness enhancement / noise suppression, 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 sharpness enhancement and noise suppression 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.

FIG. 4 is a diagram illustrating an example of amplitude non-linear compression conversion performed 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 / sharpness enhanced image processing unit 22 image monitor / image processing parameter setting unit 24 sharpness enhancement processing unit 26 smoothing process Unit 28 edge / noise mixed component extraction unit 30 edge detection unit 32 edge / noise weight coefficient calculation unit 34 edge / noise component identification / separation unit 36 noise non-linear compression LUT calculation unit 38 noise component compression processing unit 40 output image calculation unit

Claims (5)

[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. Creating image data, subtracting the smoothed image data from the sharpness-enhanced image data, creating mixed image data in which the edges of the sharpness-enhanced subject image and the noise that is also sharpness-enhanced are mixed, Edge detection is performed from the original image data to determine edge intensity data for identifying a subject edge region and a noise region.From the edge intensity data, a weighting factor for an edge region and a weighting factor for a noise region are determined. Multiplying the noise area weighting coefficient to obtain noise data of the noise area, In the noise data, a compression conversion for compressing the noise data without reversing the magnitude relationship of the amplitude of the noise data, and an amplitude non-linear compression conversion in which the compression ratio smoothly increases as the amplitude of the noise data increases. To generate noise amplitude suppression data.On the other hand, the mixed image data is multiplied by a weighting coefficient of the edge region to obtain edge enhancement data of the edge region. Adding the edge emphasis data to obtain processed image data, thereby selectively suppressing a noise component in a noise region of the original image and creating a processed image in which sharpness in an edge region of the original image is enhanced. Image processing method. Wherein said amplitude non-linear compression transformation, when the noise data and G _i, the image processing method according to claim 1 represented by the following formula (1). _{L GC (G i) = G} max · {1- exp [- (log e 2) · G i / G T]} (1) where, G _T, the noise of adjusting the characteristics of the amplitude non-linear compression transformation _Gmax is a compression threshold, and _Gmax is an amplitude adjustment constant that specifies a compression amplitude level. 3. The noise compression threshold value is determined based on a standard deviation or an average value of the noise data.
The image processing method according to 1. 4. The amplitude non-linear compression conversion is performed by using conversion data of the amplitude non-linear compression conversion in which noise amplitude suppression data for noise data is obtained in advance as a look-up table. The image processing method described in the above. 5. A sharpness enhancement processing section for performing sharpness enhancement processing on original image data to create 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 generating smoothed image data; an edge detecting unit for performing edge detection from the original image data to obtain edge intensity data for identifying a subject edge region and a noise region; and the sharpness enhancement. The smoothed image data created by the smoothing processing unit is subtracted from the sharpness enhanced image data created by the processing unit, and the edges of the subject image subjected to sharpness enhancement and noise sharpened by the sharpness are mixed. Edge for creating mixed image data
A noise mixed component extracting unit; an edge / noise weighting factor calculating unit that obtains a weighting factor for an edge region and a weighting factor for a noise region from the edge strength data obtained by the edge detecting unit; and the edge / noise mixed component extracting unit. Multiplying the mixed image data created in the above by the weighting coefficient of the noise region obtained by the edge / noise weighting coefficient calculation unit to obtain noise data of the noise region; An edge-noise component identification / separation unit for multiplying the edge area weighting coefficient obtained by the noise weight coefficient calculation unit to obtain edge-enhanced data of the edge area; and the magnitude relationship of the amplitude of the noise data is reversed. Compression compression for compressing the amplitude of the noise data,
A noise non-linear compression LUT calculation unit that obtains conversion data of amplitude non-linear compression conversion in which the compression ratio smoothly increases as the amplitude of the noise data increases and creates a look-up table; The edge / noise component identification /
A noise component compression processing unit that performs a non-linear compression process on the noise data obtained by the separation unit to obtain noise amplitude suppression data; and a noise component compression processing unit that obtains smoothed image data created by the smoothing processing unit. And an output image calculation unit that adds processed edge amplitude data obtained by the edge / noise component identification / separation unit and outputs processed image data, and outputs a noise component in a noise region of the original image. An image processing apparatus that selectively generates image data that enhances sharpness in an edge region of an original image.