JP2002074356A - Method and device for processing picture and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To remove high-frequency noise and to prevent the deterioration of information with large contrast such as an edge when performing frequency emphasizing processing. SOLUTION: A blurred picture signal Sus is obtained from an original picture signal Sorg and the signal Sus is subtracted from the signal Sorg to obtain a signal Sorg-Sus. The signal Sorg-Sus is converted by a nonlinear conversion function(f) by which a value after conversion is reduced with the increase of the value to obtain a converted signal f (Sorg-Sus). This is multiplied by an emphasizing coefficient β (Sorg) and subtracted from the signal Sorg. That is, Sproc=Sorg-β (Sorg)×f (Sorg-Sus) is calculated.

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 performing a frequency emphasis process for emphasizing a predetermined frequency component on an image signal, and a computer-readable recording program for causing a computer to execute the image processing method. It concerns a possible recording medium.

[0002]

2. Description of the Related Art A number of image processings for improving a diagnostic performance of a radiation image by performing frequency emphasis processing or dynamic range compression processing using an unsharp mask image signal (hereinafter referred to as a blurred image signal) by the present applicant. Methods and apparatuses have been proposed (JP-A-55-163472 and JP-A-55-8795).
No. 3, JP-A-3-222577, No. 10-75395, No. 10-75364
No. 10-171983). For example, in the frequency emphasis processing, a predetermined spatial frequency component of the original image signal is enhanced by adding a value obtained by subtracting the blurred image signal Sus from the original image signal Sorg and an enhancement coefficient β to the original image signal Sorg. Is what you do. This is expressed by the following equation (1).

Sproc = Sorg + β × (Sorg−Sus) (1) (Sproc: frequency-enhanced signal, Sorg: original image signal, Sus: blurred image signal, β: enhancement coefficient) A frequency emphasis processing method in which the value of the emphasis coefficient β is negative has also been proposed by the present applicant (Japanese Patent Laid-Open No.
1-106277, 1-239681). Thus, the enhancement coefficient β
By setting the value of to be negative, high frequency noise can be removed from the original image.

Japanese Patent Application Laid-Open No. 10-75395 discloses a method for preventing the occurrence of an artifact in a frequency-enhanced signal by adjusting a frequency response characteristic of an addition signal to be added to an original image signal. Has been proposed. According to this method, first, a plurality of blurred image signals having different sharpness, that is, different frequency response characteristics are created, and a difference between the blurred image signal and two signals in the original image signal is calculated. A plurality of band-limited image signals representing frequency components of a limited frequency band are created, and the band-limited image signals are further converted to have desired sizes by different conversion functions. The addition signal is created by integrating the suppressed band-limited image signals. This processing can be represented, for example, by the following equation (2).

Sproc = Sorg + β (Sorg) × Fusm (Sorg, Sus1, Sus2,... Susn) Fusm (Sorg, Sus1, Sus2,... Susn) = f ₁ (Sorg−Sus1) + f ₂ (Sus1−Sus2) +. _k (Susk-1−Susk) +... + f _n (Susn−1−Susn) (2) (however, Sproc: processed image signal Sorg: original image signal Susk (k = 1 to n): blurred image signal f _k (k = 1 to n): a conversion function for converting each band-limited image signal β (Sorg): an enhancement coefficient determined based on the original image signal) Further, JP-A-10-75364 discloses a dynamic range compression process. When applying, a method has been proposed to prevent the occurrence of artifacts in the processed signal. As described in Japanese Patent Application Laid-Open No. H10-75395, a plurality of band-limited image signals are generated, and a signal (low-frequency component) related to a low-frequency component of an original image signal is generated based on the band-limited image signal. Signal), and adds a signal related to the low-frequency component to the original image signal to perform a dynamic range compression process. This processing can be represented, for example, by the following equation (3).

Sproc = Sorg + D (Sorg−Fdrc (Sorg, Sus1, Sus2,... Susn)) (3) Fdrc (Sorg, Sus1, Sus2,... Susn) = ｛f _d1 (Sorg−
Sus1) + f _d2 (Sus1−Sus2) +... + F _dk (Susk−1−Su
sk) +... + f _dn (Susn−1−Susn)｝ (where, Sproc: processed image signal Sorg: original image signal Susk (k = 1 to n): blurred image signal f _dk (k = 1 to n): A conversion function D (Sorg-Fdrc) used to obtain a low-frequency component signal: a dynamic range compression coefficient determined based on the low-frequency component signal (D is a function for converting Sorg-Fdrc). No. -171983 proposes a method for preventing the occurrence of artifacts in a processed signal when frequency enhancement processing and dynamic range compression processing are performed simultaneously. As described in Japanese Patent Application Laid-Open No. H10-75395, this method creates a plurality of band-limited image signals, and based on the band-limited image signals, signals related to high-frequency components of the original image signal (high-frequency component signals). A low-frequency component signal is obtained, and a signal related to the high-frequency component and a signal related to the low-frequency component are added to the original image signal to perform the frequency emphasis processing and the dynamic range compression processing. This processing can be represented, for example, by the following equation (4).

Sproc = Sorg + β (Sorg) · Fusm (Sorg, Sus1, Sus2,... Susn) + D (Sorg−Fdrc (Sorg, Sus1, Sus2,... Susn)) (4) Fusm (Sorg, Sus1, Sus2,. Susn) = {f _u1 (Sorg-
_{Sus1) + f u2 (Sus1 -Sus2} ) + ... + f uk (Susk-1-Su
sk) +... + f _un (Susn−1−Susn)｝ Fdrc (Sorg, Sus1, Sus2,... Susn) = ｛f _d1 (Sorg−
Sus1) + f _d2 (Sus1−Sus2) +... + F _dk (Susk−1−Su
sk) +... + f _dn (Susn−1−Susn)｝ (where Sproc: processed image signal Sorg: original image signal Susk (k = 1 to n): blurred image signal f _uk (k = 1 to n): A conversion function f _dk (k = 1 to n) used to obtain a high-frequency component signal: a conversion function β (Sorg) used to obtain a low-frequency component signal: an enhancement coefficient D (based on an original image signal) Sorg-Fdrc): Dynamic range compression coefficient determined based on the low frequency component signal (D is a function for converting Sorg-Fdrc)) In these frequency emphasis processing and dynamic range compression processing (hereinafter referred to as conversion processing), By changing the definition of the conversion function for converting the band-limited image signal, the frequency response characteristics of the added signal to be added to the original image signal can be adjusted. Therefore, depending on the definition of each conversion function,
It is possible to obtain a processed image signal having a desired frequency response characteristic such as prevention of occurrence of an artifact.

[0008]

DISCLOSURE OF THE INVENTION Problems to be Solved by the Invention
No. 77, etc., the method of making the enhancement coefficient negative at the time of frequency enhancement processing can remove high frequency noise from the original image, but high frequency information such as edges with large contrast contained in the image Is also reduced,
The resulting image is blurred.

The present invention has been made in view of the above circumstances, and an image processing method and apparatus capable of removing high-frequency noise when performing frequency emphasis processing and preventing deterioration of information having a large contrast such as edges. It is an object of the present invention to provide a computer-readable recording medium in which a program for causing a computer to execute an image processing method is recorded.

[0010]

A first image processing method according to the present invention is an image processing method for performing frequency emphasis processing on an original image signal representing an original image. A high-frequency signal representing a high-frequency component is created, the high-frequency signal is converted by a conversion function that suppresses the value as the signal value increases, a conversion signal is obtained, and the conversion signal is subtracted from the original image signal and processed. It is characterized by obtaining an image signal.

The "conversion function" is a function that suppresses the signal value as the signal value increases.
It is preferable to use a non-linear function as described in 8395.

The expression "subtracting the converted signal from the original image signal" may mean subtracting the converted signal from the original image signal as it is. The converted signal is multiplied by an enhancement coefficient, and the converted signal is multiplied by the enhancement coefficient. The signal may be subtracted from the original image signal.

According to a second image processing method of the present invention, in an image processing method for performing frequency enhancement processing on an original image signal representing an original image, a plurality of band-limited image signals are created from the original image signal. Each of the band-limited image signals is converted by a conversion function that suppresses the value as the signal value is larger to obtain a plurality of converted band-limited image signals, and the added converted signal is added by adding the plurality of converted band-limited image signals. And obtaining a processed image signal by subtracting the addition conversion signal from the original image signal.

When "band-limited image signal is created by performing multi-resolution conversion of original image signal", the original image signal is divided into a plurality of frequency bands by a Laplacian pyramid decomposition by a Laplacian pyramid method or a wavelet transform. A method of converting the signal into a signal representing response characteristics can be used.

The Laplacian pyramid method is described in, for example, JP-A-5-244508, JP-A-6-96200, and JP-A-6-301766. This Laplacian pyramid is approximated by a Gaussian function to an original image. After performing a masking process using such a mask, the image is sub-sampled and the number of pixels is thinned out to halve the number of pixels, thereby reducing the 1 /
A blurred image having a size of 4 is obtained, a pixel having a value of 0 is interpolated into a sampled pixel of the blurred image to return the image to the original size, and this image is further subjected to a mask process using the above-described mask. The subtraction of the masked blurred image from the original image represents a frequency component of a limited frequency band of the original image signal, that is, a band representing frequency response characteristics of each of a plurality of frequency bands of the original image. This is to obtain a restricted image signal. This processing is repeated for the obtained blurred image to generate N band-limited image signals having a size of 1/2 ^2N of the original image. In addition,
The blurred image in the lowest frequency band represents a low frequency component of the original image.

When the original image signal is subjected to multi-resolution conversion by Laplacian pyramid decomposition, wavelet transform, or the like, the signal in the lowest frequency band represents low-frequency information obtained by reducing the original image. Since the image signal is not a band-limited image signal representing a frequency response characteristic for each, it is not used for processing in the present invention, or its value is used as 0.

The expression “subtracting the added conversion signal from the original image signal” may mean subtracting the added conversion signal from the original image signal as it is. The addition conversion signal is multiplied by an enhancement coefficient, and the enhancement coefficient is multiplied. The obtained addition conversion signal may be subtracted from the original image signal.

In the first and second image processing methods according to the present invention, the processed image signal is
Further, a dynamic range compression process may be performed to obtain a dynamic range compressed image signal. Further, the dynamic range compressed image signal may be subjected to a gradation conversion process to perform a gradation conversion image. A signal may be obtained.

According to a first aspect of the present invention, there is provided an image processing apparatus for performing a frequency emphasis process on an original image signal representing an original image, wherein the high frequency signal representing a high frequency component of the original image is obtained from the original image signal. A high-frequency signal generating means for generating a high-frequency signal, a converting means for converting the high-frequency signal by a conversion function that suppresses the signal value as the signal value becomes larger to obtain a converted signal, and subtracting the converted signal from the original image signal. And a subtraction means for obtaining a processed image signal.

According to a second aspect of the present invention, there is provided an image processing apparatus for performing frequency emphasis processing on an original image signal representing an original image, wherein a plurality of band-limited image signals are created from the original image signal. A limited image signal creating unit, a converting unit that converts each of the band-limited image signals by a conversion function that suppresses the value as the signal value increases, and obtains a plurality of converted band-limited image signals; The image processing apparatus further includes an adding unit that adds an image signal to obtain an addition conversion signal, and a subtraction unit that subtracts the addition conversion signal from the original image signal to obtain a processed image signal.

In the first and second image processing devices according to the present invention, the processed image signal is
The image processing apparatus may further include dynamic range compression processing means for performing dynamic range compression processing to obtain a dynamic range compressed image signal. The image processing apparatus may further include a gradation processing unit for obtaining a tone-converted image signal.

The first and second image processing methods according to the present invention may be provided by being recorded on a computer-readable recording medium as a program for causing a computer to execute the first and second image processing methods.

[0023]

According to the first image processing method and apparatus of the present invention, a high-frequency signal representing a high-frequency component of an original image is created, and the high-frequency signal is converted by a conversion function that suppresses the value as the signal value increases. The converted signal is obtained by conversion, and the converted signal is subtracted from the original image signal to obtain a processed image signal.

According to the second image processing method and apparatus of the present invention, a band-limited image signal is created from an original image, and the band-limited image signal is converted by a conversion function that suppresses the value as the signal value increases. The conversion is performed to obtain a converted band-limited image signal. Then, the converted band-limited image signal is added to obtain an added converted signal, and the added converted signal is subtracted from the original image signal to obtain a processed image signal.

Here, a portion having a large contrast contained in the original image has a relatively large signal value in the high-frequency signal or the band-limited image signal. According to the present invention, when a high-frequency signal or a band-limited image signal is converted by a conversion function, the signal value is suppressed as the signal value increases, so that the signal value is suppressed even in a portion where the contrast included in the original image is large. It will be. Therefore, by subtracting the converted signal or the added converted signal from the original image signal, high-frequency noise can be removed from the original image, and information can be prevented from being reduced even in a portion having a large contrast.

Further, by subjecting the processed image signal to a further dynamic range compression process, it is possible to obtain an image free from crushing in the low density region and the high density region.

Further, by subjecting the image signal subjected to the dynamic range compression processing to gradation conversion processing, an image having good contrast can be obtained.

[0028]

Embodiments of the present invention will be described below in detail with reference to the drawings. The image processing apparatus described below uses a blurred image signal for an image signal obtained by reading a radiation image of a human body recorded on a stimulable phosphor sheet so that the image becomes an image suitable for diagnosis. Then, the processed image signal is mainly recorded on a film and used for diagnosis.

FIG. 1 is a schematic block diagram showing the configuration of the image processing apparatus according to the first embodiment of the present invention. The image processing device 1 includes an original image signal Sor obtained by a reading device or the like.
a blurred image signal generating means 2 for generating a blurred image signal Sus representing a blurred image of the original image from g, and a high-frequency signal Sorg-Sus representing a high-frequency component of the original image by subtracting the blurred image signal Sus from the original image signal Sorg A subtractor 3 for obtaining a high frequency signal Sorg-Sus by a non-linear conversion function f to obtain a conversion signal f (Sorg-Sus);
(Sorg−Sus) is multiplied by a positive enhancement coefficient β (Sorg) to obtain an enhanced conversion signal β (Sorg) · f (Sorg−Sus), and further an enhanced conversion signal β (Sorg) · f from the original image signal Sorg.
(Sorg−Sus) is subtracted to obtain a processed image signal Sproc. Note that the blurred image signal creating means 2 and the subtractor 3 are high-frequency signal creating means according to the present invention,
The converter 4 is regarded as a conversion unit according to the present invention, and the arithmetic unit 5 is regarded as a subtraction unit according to the present invention.

The blurred image signal generating means 2 averages the signal values of N × N scanning points around each pixel based on the original image signal Sorg to obtain a blurred image signal Sus.

FIG. 2 shows an example of the conversion function f used in the converter 4. This conversion function f is represented by the signal Sorg-Sus
Is a non-linear function in which the slope is 1 when the absolute value of is smaller than the threshold Th1, and is smaller than 1 when the absolute value is larger than the threshold Th1.

The arithmetic unit 5 stores a correspondence table between the original image signal Sorg and the enhancement coefficient β (Sorg), and the enhancement coefficient β (Sor) corresponding to each pixel value of the original image signal Sorg.
g) is multiplied by the transformed signal f (Sorg-Sus).

The processing performed in the image processing apparatus 1 according to the first embodiment is represented by the following equation (5).

Sproc = Sorg−β (Sorg) × f (Sorg−Sus) (5) (Sproc: processed image signal, Sorg: original image signal, Su
s: blurred image signal, β (Sorg): enhancement coefficient, f: conversion function) Next, the operation of the first embodiment will be described. FIG.
4 is a flowchart showing the operation of the first embodiment, and FIG. 4 is a diagram for explaining the operation of the first embodiment. First, a blurred image signal Sus is created from the original image signal Sorg (step S1). Here, FIG. 4A shows a profile of an edge portion of the original image signal Sorg. The blurred image signal Sus obtained from such an original image signal Sorg
Is as shown in FIG. 4 (b). FIG.
As shown in (b), in the blurred image signal Sus, the edge portion in the original image signal Sorg is blurred.

Next, the original image signal Sor
The blurred image signal Sus is subtracted from g to obtain a high-frequency signal Sorg−
Sus is obtained (step S2). High frequency signal Sorg-
FIG. 4C shows the Sus profile. As shown in FIG. 4C, the high-frequency signal Sorg-Sus has a profile having a relatively large value at an edge portion. And
In the converter 4, the high-frequency signal Sorg-Sus is converted by the conversion function f shown in FIG.
s) is obtained (step S3). Here, the conversion function f
Is a non-linear function whose slope is 1 when the absolute value of the high-frequency signal Sorg-Sus is smaller than the threshold Th1, and whose slope is smaller than 1 when the absolute value of the high-frequency signal Sorg-Sus is larger than the threshold Th1. Therefore, the converted signal f (Sorg-Sus) is shown in FIG.
As shown in (d), the high-frequency signal Sorg-Sus has a profile in which the signal value of a portion exceeding the threshold Th1 (corresponding to the edge of the original image) is suppressed.

Then, in the arithmetic unit 5, the converted signal f
(Sorg−Sus) is multiplied by an enhancement coefficient β (Sorg), and is subtracted from the original image signal Sorg to obtain a processed image signal Sproc. FIG. 4E shows the profile of the processed image signal Sproc. As shown in FIG. 4E, in the processed image signal Sproc, the high-frequency noise in a flat portion having a constant signal value is removed, and the contrast of the edge portion is also maintained.

As described above, in the first embodiment,
After converting the high-frequency signal Sorg-Sus by the nonlinear function f shown in FIG. 3, the processed image signal Sproc is obtained by multiplying by the enhancement coefficient β (Sorg) and subtracting it from the original image signal Sorg. High-frequency noise can be removed from the image, and a processed image signal Sproc can be obtained without reducing information even in a portion having a large contrast.

Next, a second embodiment of the present invention will be described. FIG. 5 is a schematic block diagram showing the configuration of the image processing device according to the second embodiment of the present invention. As shown in FIG. 5, an image processing apparatus 1 according to a second embodiment of the present invention
0 designates a band-limited image signal creating means 12 for creating a band-limited image signal representing frequency response characteristics of each of a plurality of frequency bands of the original image signal Sorg, and specifies the original image signal Sorg based on the band-limited image signal. And a frequency emphasis processing means 13 for performing a frequency emphasis process for emphasizing the frequency of the signal to obtain a processed image signal Sproc.

First, the process of creating a band-limited image signal will be described in detail. FIG. 6 is a block diagram illustrating an outline of the band-limited image signal creation process, and FIG. 7 is a diagram schematically illustrating the band-limited image signal creation process. In the present embodiment, it is assumed that the band-limited image signal is created by the Laplacian pyramid method described in, for example, Japanese Patent Application Laid-Open No. 5-244508. First, as shown in FIG. 6, the band-limited image signal creating unit 12 of FIG. 5 compares the original image signal Sorg with the original image signal Sorg in the main scanning direction (X direction) and the sub-scanning direction when the original image signal Sorg is obtained. Scan direction (Y direction)
(See FIG. 8) with respect to the image signal resolution is lower than the original image signal Sorg by performing a filtering process L ₁ (hereinafter, referred to as low-resolution image signal) Create and then to the low-resolution image signal L ₁ low resolution image signals L _k of the low-resolution image signal L ₁ further create a low resolution image signal L ₂ resolution than sequential same filtering process is repeated for each resolution and later subjected to similar filtering Te (K = 1 to n). Then, in the interpolation processing unit 21, the low-resolution image signal L _k obtained at each stage of the filtering process, is subjected to interpolation processing so that each doubling the number of pixels, sharpness different blur The image signals Sus1 to Susn (hereinafter Susk)
(Represented by k = 1 to n)). Thereafter, the low-resolution image signal L _k−1 , the blur image signal Susk and the original image signal Sorg having the corresponding pixel numbers are subtracted by the subtractor 22.
And obtains the difference between the blurred image signal Sus1, this is a band-limited image signals B _k.

In the second embodiment, a filter substantially corresponding to a one-dimensional Gaussian distribution is used as a filter for the filtering process. That is, the filter coefficient of the filter is determined according to the following equation (6) regarding the Gaussian signal.

[0041]

(Equation 1) The reason why a filter substantially corresponding to the one-dimensional Gaussian distribution is used is that the Gaussian signal has good localization in both the frequency space and the real space. Note that the 5 × 1 one-dimensional filter when σ = 1 in the above equation (6) is as shown in FIG.

The filtering process is performed on the original image signal Sorg or every other pixel on the low-resolution image signal as shown in FIG. Such every other pixel of the filtering process in the X direction, by performing in the Y direction, the number of pixels low-resolution image signal L ₁ is next 1/4 of the original image, the low-resolution image signal obtained by the filtering process By repeatedly performing this filtering process, n low-resolution image signals L _k (k =
1 to n), the number of pixels is 1 / of the original image signal Sorg, respectively.
It becomes an image signal of ^22k .

Next, a description will be given interpolation process performed on a low-resolution image signal L _k obtained in this manner. As an interpolation calculation method for performing the interpolation processing, B
Although various methods such as a method using a spline may be used, in the present embodiment, since a low-pass filter based on a Gaussian signal is used in the filtering processing, the Gaussian signal is used for the interpolation operation. Specifically, in the following equation (7), σ = 2 ^k−1
Is used.

[0044]

(Equation 2) For example, when interpolating the low resolution image signal L ₁ is, k = 1
Therefore, σ = 1. In this case, a filter for performing the interpolation process is a 5 × 1 one-dimensional filter as shown in FIG. The interpolation process, first, as the same number of pixels and the low-resolution image signal L ₁ original image by a value in every other pixel for low-resolution image signal L ₁ is interpolated by one pixel 0 expanded, then carried out by subjecting to a filtering process by the one-dimensional filter shown in FIG. 11 described above with respect to the interpolated low resolution image signal L _1.

[0045] Similarly, performing this interpolation enlargement processing for all of the low-resolution image signal L _k. When interpolating the low resolution image signal L _k , 3 × 2 ^k −1 based on the above equation (7).
Create a length of the filter, by a value between each pixel of the low resolution image signals L _k interpolates one pixel of 0, 1 step High Resolution low resolution image signals L _k-1 identical to the expanded so that the number of pixels, by 3 × 2 ^k -1 in length of the filter for low-resolution image signal L _k which pixels are interpolated in this value is 0, then interpolation based expansion by performing a filtering process To obtain a blurred image signal Susk.

Next, the blurred image signal Susk generated as described above is subtracted from the low-resolution image signal L _k-1 having the corresponding number of pixels, and the band-limited image signal B _k (k =
1 to n) are obtained. Note that the band-limited image signal B _k is _represented by the following equation (8).

B ₁ = Sorg−Sus1 B ₂ = L ₁ −Sus ₂ B ₃ = L ₂ −Sus ₃ (8) B _k = L _k −Susk Specifically, as shown in FIG. If resolution image signal L ₁ ~L ₅ is obtained, by performing an interpolation process first on the low-resolution image signal L ₅ of the lowest resolution, the blurred image signal Sus5 having the same number of pixels and the low-resolution image signal L ₄ create. Then, obtain a band-limited image signals B ₅ subtracts the unsharp image signals Sus5 from the low resolution image signals L _4. The following sequence _{L 3 -Sus4, L 2 -Sus3,} L 1 -Sus2, Sorg-
Performing operations Sus1, obtaining a band-limited image signals B ₁ ~B _5. Here, the low-resolution image signal L _k (L ₅ ) having the lowest resolution
Represents low-frequency information obtained by reducing the original image, but is not used in subsequent calculations.

Next, the conversion process will be described which is performed by using the band-limited image signals B _k calculated as described above. FIG. 12 is a schematic block diagram showing the configuration of the frequency enhancement processing means 13 together with the band-limited image signal creation means 12, and FIG. 13 is a diagram schematically showing the frequency enhancement processing. FIG.
As shown in, the band-limited image signals B _k created in the band-limited image signal forming means 12, it is suppressed to a desired size by the transformation function f ₁ ~f _n in converter 32, converted signal (Conversion band limited image signal) f _k
B _k (k = 1 to n) is obtained. What has the characteristic shown in FIG. 2 as in the first embodiment may be used as the conversion function f ₁ . The conversion function may be the same for each band-limited image signal, but may be different for each signal. Also, the converter 32 is considered as a conversion means according to the present invention.

Then, of the converted signals f _k B _k ,
With transformed signal f _n B _n of lowest resolution is a high-frequency signal Sn, so the high-frequency signal Sn is converted signals f _n-1 B _n-1 and the same number of pixels of the one stage high resolution, interpolation processing In the means 33, interpolation processing is performed in the same manner as in the interpolation processing means 21 to obtain an enlarged high-frequency signal Sn '. Thereafter, the converted signal f _n-1 B _n-1 and the enlarged high-frequency signal Sn '
Are added in the adder 34, and the high-frequency signal Sn−
1 is obtained. Then, an expanded high-frequency signal Sk-1 'is obtained by interpolation and expansion of the high-frequency signal Sk-1, and a high-frequency signal Sk-2 is obtained by adding the expanded high-frequency signal Sk-1' and the converted signal f _k-1 B _k-1. Are repeatedly obtained to obtain a high-frequency signal (addition conversion signal) S1 having the highest resolution. Note that the interpolation processing unit 33 and the adder 34 are considered as an addition unit according to the present invention.

More specifically, as shown in FIG. 13, when five levels of band-limited image signals B _{1 to} B ₅ are obtained, first, converted signals f ₁ B _{1 to} f ₅ B ₅ are obtained. , The converted signal f ₅ B _{5 of} the lowest resolution is used as the high-frequency signal S ₅ . Then, the converted signal f of one step high resolution is applied to the high frequency signal S5.
₄ B ₄ interpolation process to be the same number of pixels and is subjected enlarged RF signal S5 'is obtained. And the converted signal f
₄ B ₄ and larger high-frequency signal S5 'and is added, the high-frequency signal S4 is obtained. Hereinafter, similarly, the high frequency signals S3 and S
2 is obtained, and finally a high-frequency signal S1 having the highest resolution is obtained.

Thus, the highest resolution high frequency signal S
When 1 is obtained, the following equation (9) is obtained in the arithmetic unit 35.
As shown in the figure, the emphasis coefficient β (Sorg) is multiplied to obtain an emphasis conversion signal β (Sorg) · S1, and this emphasis conversion signal β (Sorg) · S1 is subtracted from the original image signal Sorg to obtain a processed image. The signal Sproc is obtained. The computing unit 35
Is considered as a subtraction means according to the present invention.

Sproc = Sorg−β (Sorg) · S1 (9) (however, Sproc: processed image signal Sorg: original image signal β (Sorg): enhancement coefficient) Next, the operation of the second embodiment will be described. . FIG.
4 is a flowchart showing the operation of the second embodiment, FIG.
FIGS. 5 and 16 are diagrams for explaining the operation of the second embodiment. In the operation explanatory diagram, description will be made assuming that a three-stage blurred image signal is generated. First, an original image signal Sorg is input from a reading device or the like to the image processing device 10 (step S11). Original image signal Sorg is band-limited image signals B _k representative of the frequency response characteristics for each frequency band where the original image signal Sorg is input into the band-limited image signal forming means 12 is created (step S12). FIG. 15A shows a profile of an edge portion of the original image signal Sorg. Such an original image signal Sorg
The profiles of the blurred image signals Sus1, Sus2, and Sus3 obtained from are as shown in FIGS. 15B, 15C, and 15D, respectively. As shown in FIGS. 15 (a) to 15 (c), in the blurred image signals Sus1 to Sus3, the original image signal Sorg
Is blurred. Original image signal S
FIG. 15 shows the profiles of the band-limited image signals B _{1 to} B ₃ obtained from the org and the blurred image signals Sus 1 to Sus ₃ .
(E), (f) and (g) show. Here, B ₁ = Sorg−
Sus1, a _{B 2 = Sus1-Sus2, B} 3 = Sus2-Sus3. As shown in FIGS. 15E to 15G, the band-limited image signals B _{1 to} B ₃ have profiles having relatively large values at portions corresponding to edges of the original image.

Band-limited image signal B_kIs shown in FIG. 2 above.
The converted signal f_kB_kBut
(Step S13), and further, the converted signal f_kB_k
High-frequency signal S by interpolation processing to one-step high-frequency band
k and the frequency band corresponding to the high-frequency signal Sk
Converted signal f_kB_kHigh-frequency signal Sk−
1 for the converted signal f in the highest frequency band₁B₁Until
To obtain a high-frequency signal S1 (step S1).
4). Converted signal fB₁~ FB_ThreeFigure 1 shows the profile
6 (a), (b) and (c). Also, the high frequency signal S
The profile of No. 1 is shown in FIG. FIG. 16 (a)
To (c), the converted signal fB ₁~ FB_ThreeIs
Band-limited image signal B₁~ B_ThreeAt the edge of the original image
The corresponding signal has a suppressed profile
It becomes

Using the high-frequency signal S1, the calculation shown in the above equation (9) is performed to obtain a processed image signal Sproc (step S15), and the process is terminated. FIG. 16E shows the profile of the processed image signal Sproc. FIG.
As shown in FIG. 6E, in the processed image signal Sproc, the high-frequency noise in a flat portion having a constant signal value is removed, and the contrast of the edge portion is also maintained.

In the first and second embodiments, the original image signal Sorg is subjected to the frequency emphasizing process. However, the frequency-enhanced processed image signal Sorg is subjected to a further dynamic image processing. Range compression processing may be performed. Hereinafter, this will be described as a third embodiment. FIG. 17 is a schematic block diagram showing the configuration of the third embodiment. As shown in FIG. 17, the image processing apparatus 40 according to the third embodiment of the present invention performs the frequency enhancement processing on the original image signal Sorg in the same manner as the first or second embodiment. Frequency processing means 41 for obtaining a processed image signal Sproc subjected to dynamic range compression, and a dynamic range compression processing image signal Sproc1 for performing dynamic range compression processing on the processed image signal Sproc.
And dynamic range compression processing means 42 for obtaining

In the dynamic range compression processing means 42, the dynamic range compression processing is performed on the processed image signal Sproc as shown in the following equation (10).

Sproc1 = Sproc + D (Susa) (10) where Susa: blurred image signal D: dynamic range compression coefficient Note that the blurred image signal Susa is the same as the blurred image signal Sus obtained in the first embodiment. You may.

As shown in the following equation (11), a dynamic range compression process may be performed on the processed image signal Sproc.

Sproc1 = Sproc + D (Sorg−Fdrc (Sorg, Sus1, Sus2,... Susn)) (11) Fdrc (Sorg, Sus1, Sus2,... Susn) = ｛f _d1 (Sorg−
Sus1) + f _d2 (Sus1−Sus2) +... + F _dk (Susk−1−Su
sk) +... + f _dn (Susn−1−Susn)｝ (however, Susk (k = 1 to n): blurred image signal f _dk (k = 1 to n) obtained in the same manner as in the second embodiment): A conversion function D (Sorg-Fdrc) used to obtain a low-frequency component signal: a dynamic range compression coefficient determined based on the low-frequency component signal (D is a function that converts Sorg-Fdrc). Image signal Spr
A gradation conversion process may be performed on oc1. Hereinafter, this will be described as a fourth embodiment. FIG. 18 is a block diagram showing the configuration of the fourth embodiment. As shown in FIG. 18, the image processing apparatus 40 'according to the fourth embodiment of the present invention includes a frequency emphasis processing unit 41 and a dynamic range compression processing unit 42 as in the third embodiment, and further includes a dynamic range compression unit. The dynamic range compression-processed image signal S obtained by the processing unit 42
It is provided with a gradation processing means 43 for performing a gradation conversion process on proc1 to obtain a gradation-converted image signal Sproc2. In the gradation processing means 43, the following expression (1)
As shown in 2), the image signal Sproc1 subjected to the dynamic range compression processing is subjected to gradation conversion processing.

Sproc2 = γ (Sproc1) (12) where γ: gradation conversion function In the fourth embodiment, the dynamic range is determined according to the degree of gradation conversion processing performed by the gradation processing means 43. It is preferable to change the degree of the dynamic range compression processing performed by the compression processing means 42.
That is, as the contrast of the processed image signal Sproc2 obtained by the gradation conversion process increases, the images in the high-density region and the low-density region are crushed. It is preferable to change the degree of the dynamic range compression coefficient in ()).

[Brief description of the drawings]

FIG. 1 is a schematic block diagram illustrating a configuration of an image processing apparatus according to a first embodiment of the present invention.

FIG. 2 is a diagram illustrating an example of a conversion function.

FIG. 3 is a flowchart showing the operation of the first embodiment;

FIG. 4 is a diagram for explaining the operation of the first embodiment;

FIG. 5 is a schematic block diagram illustrating a configuration of an image processing apparatus according to a second embodiment of the present invention.

FIG. 6 is a block diagram illustrating an outline of a band-limited image signal creation process.

FIG. 7 is a diagram schematically showing band-limited image signal creation processing.

FIG. 8 is a diagram showing a main scanning direction and a sub-scanning direction of an original image.

FIG. 9 is a diagram illustrating an example of a filter used in the filtering process.

FIG. 10 is a diagram showing details of a low-resolution image signal creation process;

FIG. 11 is a diagram showing an example of a filter used for interpolation processing.

FIG. 12 is a schematic block diagram showing a configuration of a frequency emphasis processing unit together with a band-limited image signal creating unit;

FIG. 13 is a diagram schematically showing a conversion process.

FIG. 14 is a flowchart showing the operation of the second embodiment.

FIG. 15 is a view for explaining the operation of the second embodiment (part 1)

FIG. 16 is a view for explaining the operation of the second embodiment (part 2)

FIG. 17 is a schematic block diagram illustrating a configuration of an image processing apparatus according to a third embodiment of the present invention.

FIG. 18 is a schematic block diagram illustrating a configuration of an image processing apparatus according to a fourth embodiment of the present invention.

[Explanation of symbols]

 1,10,40 Image processing device 2 Blurred image signal generating means 3,22 Subtractor 4,32 Transformer 5,35 Arithmetic unit 12 Band-limited image signal generating means 13 Frequency emphasizing processing means 20 Filtering processing means 21 Interpolating processing means 34 Adder

Claims (15)

[Claims] 1. An image processing method for performing frequency emphasis processing on an original image signal representing an original image, comprising: creating a high-frequency signal representing a high-frequency component of the original image from the original image signal; An image processing method, comprising: obtaining a converted signal by converting with a conversion function that suppresses the signal value as the signal value increases, and obtaining a processed image signal by subtracting the converted signal from the original image signal. 2. An image processing method for performing frequency emphasis processing on an original image signal representing an original image, comprising: generating a plurality of band-limited image signals from the original image signal; A larger value is converted by a conversion function that suppresses the value to obtain a plurality of converted band-limited image signals, and the plurality of converted band-limited image signals are added to obtain an added converted signal. An image processing method, wherein a processed image signal is obtained by subtraction from an image signal. 3. The image processing method according to claim 1, wherein the processed image signal is further subjected to a dynamic range compression process to obtain a dynamic range compressed image signal. 4. The image processing method according to claim 3, wherein the image signal subjected to the dynamic range compression processing is further subjected to gradation conversion processing to obtain an image signal subjected to gradation conversion processing. 5. The image processing method according to claim 1, wherein the predetermined conversion function is a non-linear function. 6. An image processing apparatus for performing frequency emphasis processing on an original image signal representing an original image, comprising: a high-frequency signal creating unit that creates a high-frequency signal representing a high-frequency component of the original image from the original image signal; A conversion unit that converts the high-frequency signal by a conversion function that suppresses the value as the signal value increases, to obtain a conversion signal; and a subtraction unit that subtracts the conversion signal from the original image signal to obtain a processed image signal. An image processing apparatus comprising: 7. An image processing apparatus for performing frequency emphasis processing on an original image signal representing an original image, comprising: a band-limited image signal creating unit for creating a plurality of band-limited image signals from the original image signal; A conversion unit that converts the band-limited image signal by a conversion function that suppresses the value as the signal value increases, and obtains a plurality of converted band-limited image signals; and an addition conversion signal that adds the plurality of converted band-limited image signals. An image processing apparatus comprising: an adding unit that obtains the following; and a subtracting unit that obtains a processed image signal by subtracting the added converted signal from the original image signal. 8. A dynamic range compression processing means for subjecting the processed image signal to a dynamic range compression process to obtain a dynamic range compressed image signal.
The image processing apparatus according to any one of the preceding claims. 9. The image processing apparatus according to claim 8, further comprising: gradation processing means for performing gradation conversion processing on the image signal after dynamic range compression processing to obtain an image signal after gradation conversion processing. Image processing device. 10. The image processing apparatus according to claim 6, wherein the predetermined conversion function is a non-linear function. 11. An original image signal representing an original image,
A computer-readable recording medium storing a program for causing a computer to execute an image processing method for performing a frequency enhancement process, wherein the program creates a high-frequency signal representing a high-frequency component of the original image from the original image signal. Converting the high-frequency signal with a conversion function that suppresses the signal value as the signal value increases, to obtain a converted signal; and subtracting the converted signal from the original image signal to obtain a processed image signal. A computer-readable recording medium comprising: 12. An original image signal representing an original image,
A computer-readable recording medium recording a program for causing a computer to execute an image processing method for performing frequency enhancement processing, the program comprising: a step of creating a plurality of band-limited image signals from the original image signal; Converting the band-limited image signal by a conversion function that suppresses the value as the signal value increases, to obtain a plurality of converted band-limited image signals; and adding the plurality of converted band-limited image signals to obtain an added converted signal. Obtaining a processed image signal by subtracting the addition conversion signal from the original image signal. 13. The computer-readable computer according to claim 11, further comprising a step of performing a dynamic range compression process on the processed image signal to obtain a dynamic range compressed image signal. recoding media. 14. The computer-readable computer according to claim 13, further comprising a step of further performing a gradation conversion process on the image signal after the dynamic range compression processing to obtain a gradation conversion processed image signal. Recording medium. 15. The computer-readable recording medium according to claim 11, wherein the predetermined conversion function is a non-linear function.