JP2001056856A - Method and device for image processing and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To shorten the arithmetic time of a converting process such as a frequency process using a band-limited image signal. SOLUTION: A band-limited image signal generation means 2 generates band-limited image signals Bk (k=1 to n) which represent frequency response characteristics by frequency bands of an original image signal Sorg and have the numbers of pixels corresponding to the frequency bands. Each band-limited image signal Bk is converted by a converter 22 according to a conversion function fk and the converted signal fkBk is interpolated and expanded so as to have pixels as many as the band-limited image signal Bk-1 of the one-stage higher frequency band, thereby obtaining an expanded high-frequency signal Sk'. The expanded high-frequency signal Sk' and converted signal fk-1Bk-1 are added to obtain a high frequency signal Sk-1. The acquisition of the expanded high-frequency signal Sk-1' by the interpolation and expansion of the high-frequency signal Sk-1 and the acquisition of the high-frequency signal Sk-2 by the addition of the expanded high-frequency signal Sk-1' and converted signal fk-1Bk-1 are repeated to obtain a high frequency signal S1 of the highest resolution. This high-frequency signal S1 is used to perform a frequency emphasizing process, thereby obtaining a processed image signal Sproc.

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 image processing such as emphasizing a predetermined frequency component on an image signal, and a program for causing a computer to execute the image processing method. Computer-readable recording medium.

[0002]

2. Description of the Related Art In the field of image processing, when an image signal is subjected to different image processing for each frequency band, a method such as wavelet transform or Laplacian pyramid is used as means for classifying the image signal for each frequency band. Is used. Here, examples of the image processing include high-frequency separation for removing noise, and compression processing by reducing data in a frequency band with much noise. The present applicant has also proposed various image processing methods for performing image processing such as enhancing only edge components in an image using a wavelet transform (for example, JP-A-6-274615 and JP-A-6-350989). No.).

On the other hand, the Laplacian pyramid method is disclosed in, for example, JP-A-5-244508, JP-A-6-96200, and JP-A-6-30.
This Laplacian pyramid is described in, for example, No. 1766.After applying a mask process to the original image using a mask approximated by a Gaussian function, the image is sub-sampled and the number of pixels is thinned to halve the number of pixels. As a result, a blurred image having a size of 1/4 of the original image is obtained, and 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. A blurred image is obtained by performing a mask process using the mask described above, and the blurred image is subtracted from the original image to represent frequency components of a limited frequency band of the original image signal, that is, for each of a plurality of frequency bands of the original image. To obtain a band-limited image signal representing the frequency response characteristic of 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. Note that the blurred image in the lowest frequency band represents a low frequency component of the original image.

Here, in Japanese Patent Laid-Open No. Hei 5-244508, a radiographic image is decomposed into images of a plurality of frequency bands by a Laplacian pyramid to obtain band-limited image signals for each frequency band. By converting with a non-linear function and reconstructing the converted band-limited image signal together with the blurred image signal representing the blurred image in the lowest frequency band, the processed image signal in which the contrast according to the frequency band in the image has been enhanced can be obtained. Methods have been proposed to get it.

On the other hand, the present applicant has performed various image processing for improving the diagnostic performance of a radiation image by performing frequency enhancement processing or dynamic range compression processing using an unsharp mask image signal (hereinafter referred to as a blurred image signal). Methods and apparatuses have been proposed (JP-A-55-163472, 55-163472).
No. 87953, JP-A-3-222577, No. 10-75395, No. 10-7536
No. 4, 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) Has proposed a method of adjusting the frequency response characteristics of an addition signal to be added to an original image signal, thereby preventing the occurrence of an artifact in the signal subjected to the frequency emphasis processing. This method is
First, a plurality of blurred image signals having different sharpness, that is, different frequency response characteristics are created, and a difference between the two signals in the blurred image signal and the original image signal is obtained.
A plurality of band-limited image signals (i.e., band-limited image signals) representing frequency components of a limited frequency band of the original image signal are created, and the band-limited image signals are each converted to a desired size by a different conversion function. After such conversion, the added signal is created by integrating the plurality of 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−Susk) +... + F _dn (Susn−1−Susn)｝ (however, Sproc: processed image signal Sorg: original image signal Susk (k = 1 to n): blurred image Signal f _dk (k = 1 to n): Conversion function used to obtain low frequency component signal D (Sorg-Fdrc): Dynamic range compression coefficient (D is Sorg-Fdrc) determined based on low frequency component signal Japanese Patent Application Laid-Open No. 10-171983 also discloses a method for preventing the occurrence of artifacts in a processed signal when frequency enhancement processing and dynamic range compression processing are performed simultaneously. Has been proposed. 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-Susk) + ... + f un (Susn-1-Susn)} Fdrc (Sorg, Sus1, Sus2, ... Susn) = {f _d1 (Sorg−Sus1) + f _d2 (Sus1−Sus2) +... + F _dk (Susk−1−Susk) +... + F _dn (Susn−1−Susn)} (Sproc: processed image signal Sorg: original image signal Susk (k = 1 to n): blurred image signal f _uk (k = 1 to n): conversion function f _dk (k = 1 to n) used to obtain a high frequency component signal: low frequency A conversion function β (Sorg) used to obtain a component signal: an emphasis coefficient determined based on an original image signal D (Sorg-Fdrc): a dynamic range compression coefficient determined based on a 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), the original is obtained by changing the definition of a conversion function for converting a band-limited image signal. The frequency response characteristics of the addition signal to be added to the 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.

On the other hand, the blurred image signal used in the above-described conversion processing is first thinned out by subjecting pixels of the original image signal to predetermined filtering processing at predetermined intervals, and the image thus obtained is obtained. The same filtering process is repeated on the signal to create a plurality of low-resolution image signals with a further reduced number of pixels, and for each of the low-resolution image signals, the number of pixels is the same as the original image by a predetermined interpolation method. It is created by performing interpolation processing as follows. Accordingly, the blurred image signal has the same number of pixels as the original image signal, but is an image signal representing an image having lower sharpness than the original image signal.

The band-limited image signal is created, for example, by taking the difference between blurred image signals in adjacent frequency bands or by taking the difference between the original image signal and each blurred image signal. Therefore, the band-limited image signal has the same number of pixels as the original image signal, and is a signal representing the frequency response characteristic of each frequency band of the original image signal.

[0012]

In the method described in Japanese Patent Application Laid-Open No. 5-244508, the band-limited image signal in each frequency band is subjected to a conversion process by a non-linear function, and the converted band-limited image signal is processed. Is reconstructed to obtain the processed image signal, so if you want to correct the degree of conversion,
It is necessary to reconstruct the image after performing the conversion process on the band-limited image signal again, and as a result, the reprocessing takes a long time. For this reason, when the content of the image processing is variously changed while observing the CRT to set the optimal image processing content, it takes a long time until the processed image is reproduced. Operator stress is great.

The present invention has been made in view of the above circumstances, and executes an image processing method and apparatus capable of performing image processing such as frequency emphasis processing using a band-limited image signal at high speed, and executes the image processing method on a computer. It is an object of the present invention to provide a computer-readable recording medium on which a program for causing a program to be recorded is recorded.

[0014]

An image processing method according to the present invention performs image processing on an original image signal representing an original image based on a signal relating to a high frequency component of the original image to obtain a processed image signal. In the processing method, a band-limited image signal is created from the original image signal, and a signal related to a high-frequency component of the original image is obtained from the band-limited image signal based on a predetermined conversion function. , Wherein the image processing is performed on the original image signal.

Here, the “image processing” specifically includes
Frequency emphasis processing that emphasizes specific frequency components included in the original image signal, and high-density or low-density area or high-density area and low-density so as to narrow the difference between the maximum density and the minimum density of the original image, that is, the dynamic range For example, a dynamic range compression process for lowering the contrast of both regions may be used. Further, there is an example in which the frequency enhancement processing and the dynamic range compression processing are performed simultaneously. In addition,
As the “predetermined conversion function”, it is preferable to use a function according to the content of the image processing.

In the image processing method according to the present invention, it is preferable that each image represented by the band-limited image signal has a number of pixels corresponding to the frequency band.

In the image processing method according to the present invention, it is preferable that the signal relating to the high frequency component has the same number of pixels as the original image signal.

Here, "the same number of pixels as the original image signal" means that the image size of the original image and the image represented by the signal related to the high frequency component are the same.

Further, in the image processing method according to the present invention, the band-limited image signal is created by subjecting the original image signal to multi-resolution conversion, and the band-limited image signal is generated based on a predetermined conversion function. A conversion band-limited image signal is obtained by performing a conversion process, and the converted band-limited image signal is subjected to inverse multi-resolution conversion, whereby a signal relating to the high frequency component can be obtained. The inverse multi-resolution conversion corresponds to the multi-resolution conversion, and the original signal can be restored (either reversible or irreversible) by performing the inverse multi-resolution conversion. Needless to say,

Here, when the "band-limited image signal is created by performing multi-resolution conversion of the original image signal", the original image signal is divided into a plurality of frequency bands by Laplacian pyramid decomposition by the Laplacian pyramid method or wavelet transform. For example, a method of converting the signal into a signal representing each frequency response characteristic can be used.
In this case, as the “inverse multiresolution conversion”, a Laplacian pyramid reconstruction method is used when a band-limited image signal is obtained by Laplacian pyramid decomposition, and an inverse wavelet transform is performed when a band-limited image signal is obtained by wavelet conversion. It goes without saying that is used.

When the original image signal is subjected to multi-resolution conversion by Laplacian pyramid decomposition or wavelet transform, 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 preferable that the image signal is not used for processing in the present invention or the value is used as 0.

In the image processing method according to the present invention, the predetermined conversion function is preferably a non-linear function.

The image processing apparatus according to the present invention is an image processing apparatus for obtaining a processed image signal by performing image processing on an original image signal representing an original image based on a signal relating to a high frequency component of the original image. A band-limited image signal creating unit that creates a band-limited image signal from the image signal; a high-frequency component acquiring unit that obtains a signal related to a high-frequency component of the original image from the band-limited image signal based on a predetermined conversion function; Image processing means for performing the image processing on the original image signal based on a signal relating to the component.

[0024] In the image processing apparatus according to the present invention, the band-limited image signal creating means may be configured so that the image of each frequency band represented by the band-limited image signal has a number of pixels corresponding to the frequency band. Preferably, the unit is a unit that creates the band-limited image signal.

In the image processing apparatus according to the present invention, the high-frequency component obtaining means obtains a signal relating to the high-frequency component such that the signal relating to the high-frequency component has the same number of pixels as the original image signal. Is preferred.

Further, in the image processing apparatus according to the present invention, the band-limited image signal creating means is means for creating the band-limited image signal by performing multi-resolution conversion of the original image signal, The obtaining means performs a conversion process on the band-limited image signal based on the predetermined conversion function to obtain a converted band-limited image signal, and performs inverse multi-resolution conversion of the converted band-limited image signal, thereby obtaining the high-frequency signal. Preferably, it is a means for obtaining a signal regarding the component.

In this case, the multi-resolution conversion can be a conversion based on Laplacian pyramid decomposition or a wavelet conversion.

In the image processing apparatus according to the present invention, it is preferable that the high-frequency component obtaining means obtains a signal related to the high-frequency component from a band-limited image signal other than the band-limited image signal of the lowest frequency band.

Further, it is preferable that the predetermined conversion function is a non-linear function.

Further, it is preferable that the image processing is a frequency enhancement processing and / or a dynamic range compression processing.

Note that a program for causing a computer to execute the image processing method according to the present invention may be provided by being recorded on a computer-readable recording medium.

[0032]

According to the present invention, a band-limited image signal is created from an original image, a signal relating to a high-frequency component of the original image is obtained from the band-limited image signal based on a predetermined conversion function, and based on this signal, The image processing is performed on the original image signal. Therefore, the degree of image processing on the original image signal can be arbitrarily changed only by changing the level of the signal related to the high-frequency component, so that the nonlinear function is corrected as described in Japanese Patent Application Laid-Open No. 5-244508. Can easily change the degree of image processing as compared with the method of changing the degree of image processing, thereby shortening the calculation time for obtaining a processed image signal and reducing operator stress. can do.

Further, by making the image of each frequency band of the band-limited image signal have the number of pixels corresponding to the frequency band, it is possible to reduce the amount of calculation at the time of performing the conversion process. In addition, the calculation time for obtaining the processed image signal can be further reduced.

Further, by setting the number of pixels of the signal relating to the high frequency component to be the same as the number of pixels of the original image signal, the signal relating to the high frequency component can be immediately processed with respect to the original image signal without performing a process of changing the size. Image processing can be performed by the image processing, so that the image processing can be performed more efficiently.

[0035]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of an image processing method and apparatus according to 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 apparatus 1 generates a band-limited image signal representing a frequency response characteristic of each of a plurality of frequency bands of an original image from an original image signal Sorg having a predetermined resolution obtained by a reading device or the like. 2 and a processed image signal Spr by performing frequency emphasis processing for emphasizing a specific frequency on the original image signal Sorg based on the band-limited image signal.
conversion processing means 3 for obtaining oc.

First, the process of creating a band-limited image signal will be described in detail. FIG. 2 is a block diagram illustrating an outline of the band-limited image signal creation process, and FIG. 3 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. 2, the band-limited image signal creating means 2 shown in FIG. 1 performs filtering processing on the original image signal Sorg in the x direction and the y direction (see FIG. 22) of the original image by the filtering processing means 10. alms and the original image signal the image signal resolution is lower than Sorg L ₁ (hereinafter, referred to as low-resolution image signal) create and then the low and subjected to the same filtering processing on the low resolution image signals L ₁ which resolution image signal L ₁ further resolution creates a low resolution image signals L ₂ than to obtain a low-resolution image signal of the sequential same filtering process repeatedly the respective resolution L _{k (k} = 1~n) and later It is. And
In the interpolation processing unit 11, the low-resolution image signal L _k obtained at each stage of the filtering process,
Interpolation processing is performed so that the number of pixels is twice as large,
A plurality of blurred image signals Sus1 to Susn having different sharpness (hereinafter represented by Susk (k = 1 to n)) are obtained. Thereafter, the difference between the low-resolution image signal Lk _-1 and the blurred image signal Susk and the original image signal Sorg and the blurred image signal Sus1 having the corresponding numbers of pixels are obtained by the subtractor 12, and the difference is obtained by the band-limited image signal B. _Let it be _k .

In this 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 (5) regarding the Gaussian signal.

[0039]

(Equation 1) This is because the Gaussian signal has good localization in both the frequency space and the real space. For example, a 5 × 1 one-dimensional filter when σ = 1 in the above equation (5) is as shown in FIG. It becomes something.

As shown in FIG. 5, the filtering process is performed on the original image signal Sorg or every other pixel on the low resolution image signal. Such every other pixel of the filtering process in the x-direction, by performing 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, the interpolation processing will be described which is performed on the 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 (6), σ = 2 ^k−1
Is used.

[0042]

(Equation 2) For example, when interpolating the low resolution image signal L ₁ is, k = 1
Therefore, σ = 1. In this case, the filter for performing the interpolation processing is a 5 × 1 one-dimensional filter as shown in FIG. In this interpolation processing, first, the low-resolution image signal L
Low value to every other pixel with respect to ₁ low-resolution image signal L ₁ to expand so that the original image and the same number of pixels by interpolating one pixel of 0, then, is this interpolated is performed by performing a filtering process by the one-dimensional filter shown in FIG. 6 described above with respect to resolution image signal L _1.

[0043] 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 (6).
Create a length of the filter, by a value between each pixel of the low resolution image signals L _k interpolates one by 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 (7).

B ₁ = Sorg−Sus1 B ₂ = L ₁ −Sus ₂ B ₃ = L ₂ −Sus ₃ (7) 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 bandwidth limitation calculated as described above
Image signal B_kThe conversion process performed using
You. In the present embodiment, the frequency emphasis processing is performed as the conversion processing.
Process. FIG. 7 shows the configuration of the conversion processing means 3.
Schematic block shown together with area-limited image signal creating means
FIG. 8 and FIG. 8 are diagrams schematically showing the conversion process. As shown in FIG.
As described above, the band-limited image signal
Band-limited image signal B _kIs converted by the converter 22
Function f₁~ F_nIs suppressed to a desired size by
And the converted signal f_kB_k(K = 1 to n) are obtained. Seki
Number f₁9 is shown in FIG. This function f₁Is a band-limited image
When the absolute value of the signal is smaller than the threshold Th1, the slope is 1
When the inclination is larger than the threshold Th1, the inclination is larger than 1.
It is a nonlinear function that becomes smaller. This function returns
Although it may be the same in the area-limited image signal,
It may be different for each signal.

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 23, the interpolation processing is performed in the same manner as in the interpolation processing means 11, and the expanded high-frequency signal Sn 'is obtained. Thereafter, the converted signal f _n-1 B _n-1 and the enlarged high-frequency signal Sn '
Are added in the adder 24, 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 the high-resolution high-frequency signal S1.

More specifically, as shown in FIG. 8, when five-stage 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 ₄ of one-step high resolution is applied to the high-frequency signal S5.
Interpolation so that the B ₄ and the same number of pixels is larger RF signal S5 'is obtained decorated. 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.

It should be noted that the converter 22, the interpolation processing means 23 and the adder 24 are considered as high frequency component acquisition means according to the present invention.

In this manner, the highest resolution high frequency signal S
Is obtained in the arithmetic unit 25 as the image processing means of the present invention, the degree of frequency emphasis corresponding to the value of the original image signal Sorg with respect to the high-frequency signal S1 as shown in the following equation (8). Is multiplied by a degree of emphasis β as a parameter indicating the high frequency signal S1 further multiplied by the degree of emphasis β
Is added to the original image signal Sorg and the processed image signal Spr
oc is obtained.

Sproc = Sorg + β (Sorg) · S1 (8) (where, Sproc: an image signal in which high-frequency components are enhanced Sorg: an original image signal β (Sorg): an enhancement coefficient determined based on the original image signal) , By changing the value of the emphasis degree β (Sorg),
The degree of image processing of the processed image signal Sproc can be arbitrarily changed.

Next, the operation of the first embodiment will be described. FIG. 10 is a flowchart showing the operation of the first embodiment. First, an original image signal Sorg is read from a reading device or the like.
Is input to the image processing apparatus 1 (step S1). 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 2 is generated (Step S2). The band-limited image signal B _k is converted by a conversion function as shown in FIG. 9 to obtain a converted signal f _k B _k (step S3), and further, one of the converted signals f _k B _k
The acquisition of the high-frequency signal Sk by the interpolation processing into the step high frequency band and the acquisition of the high-frequency signal Sk-1 by adding the high-frequency signal Sk and the converted signal f _k B _k of the corresponding frequency band have been converted to the highest frequency band. by repeating until the signal f ₁ B _1, to obtain a high-frequency signal S1 (step S4). The high frequency signal S1 is used to perform the calculation shown in the above equation (8) to obtain a processed image signal Sproc (step S8).
5), end the processing.

As described above, in the first embodiment,
Since the degree of image processing of the processed image signal Sproc can be changed only by changing the value of the enhancement degree β (Sorg), the nonlinear function is corrected as described in Japanese Patent Application Laid-Open No. 5-244508. This makes it possible to easily change the degree of image processing as compared with a method of changing the degree of image processing, thereby shortening the calculation time for obtaining the processed image signal Sproc. Therefore, for example, even when the content of the image processing is switched and the processed image signal Sproc is continuously displayed on the CRT or the like, the processing time can be reduced and the stress on the operator can be reduced.

Here, the calculation amount of the process for obtaining the high-frequency signal S1 according to the first embodiment is compared with the conventional calculation amount.
FIG. 11 is a diagram schematically showing a conventional process described in, for example, Japanese Patent Application Laid-Open No. 10-75395. As shown in FIG.
In the conventional processing, the low-resolution image signal L
seeking _k, by performing interpolation processing on the low resolution image signals L _k, to obtain a blurred image signal Susk having an original image signal Sorg same number of pixels and, between the unsharp image signals Susk and the original image signal Sorg Is subtracted from the blurred image signal Sus1 to obtain a band-limited image signal B _k having the same number of pixels as the original image.
The resulting, after conversion by the conversion function for each band-limited image signals B _k, by adding the multiplied by the emphasis coefficient β with respect to the band-limited image signal after the conversion to the original image signal Sorg , And a processed image signal Sproc. This processing is shown in the following equation (9).

Sproc = Sorg + β (Sorg) × Fusm (Sorg, Sus1, Sus2,... Susn) Fusm (Sorg, Sus1, Sus2,... Susn) = f ₁ (Sorg−Sus1) + f ₂ (Sus1−Sus2) +. + F _k (Susk−1−Susk) +... + F _n (Susn−1−Susn) (9) (however, Sproc: an image signal in which high-frequency components are emphasized Sorg: an original image signal Susk (k = 1 to n): Blurred image signal (the same number of pixels as the original image) f _k (k = 1 to n): conversion function for converting each band-limited image signal β (Sorg): enhancement coefficient determined based on the original image signal In the conventional processing, the low-resolution image signal L
_{Since k} is subjected to interpolation processing so as to have the same number of pixels as the original image signal Sorg, for example, the number of pixels of the original image is 10
24 × 1024, and an original image signal S representing this original image
When the low-resolution image signals L _{1 to} L ₆ in six stages are obtained from org, assuming that the interpolation processing is to calculate one pixel using 16 pixels in a 4 × 4 range, the interpolation processing is performed. The calculation amount is (1024 x 1024) x 16 x 6 = 1006
63296 operations are required. On the other hand, in the present embodiment, the blurred image signal Susk has the number of pixels corresponding to the frequency band, and interpolation processing is performed when expanding the high-frequency signal Sk obtained from the converted signal f _k B _k. Therefore, the calculation amount is ((1024 × 1024) + (5
(12 × 512) + (256 × 256) + (128 × 128) + (64 × 64) + (32 × 32))
× 16 = 22364160. Note that the blurred image signal Su is actually
Since the interpolation processing is also performed when obtaining sk, the total calculation amount of the interpolation processing is 44728320 times. Since this calculation amount is about 2.25 times smaller than the conventional calculation amount,
The operation time can be reduced by about 2.25 times.

In the present invention, the method of creating the band-limited image signal is not limited to the method described in the first embodiment.
The band-limited image signal may be created by the method described in Japanese Patent No. 5395.

In the first embodiment, the band-limited image signal representing the characteristic of each frequency band is obtained from the original image signal Sorg by the Laplacian pyramid method. As shown, a band-limited image signal may be obtained by wavelet transform. Hereinafter, an embodiment of image processing using wavelet transform will be described as a second embodiment.

FIG. 12 is a schematic block diagram showing the configuration of an image processing apparatus according to the second embodiment of the present invention. FIG.
As shown in the figure, an image processing apparatus 31 according to a second embodiment of the present invention includes a wavelet transform unit 32 that performs a wavelet transform on an original image signal Sorg having a predetermined resolution obtained by a reading device or the like, and a wavelet transform. Conversion processing means 33 for performing a frequency emphasis process for emphasizing a specific frequency on the original image signal Sorg based on the obtained signal to obtain a processed image signal Sproc. Note that the main scanning direction and the sub-scanning direction in the second embodiment are directions shown in FIG. 22 with respect to the original image.

FIG. 13 is a schematic block diagram showing the configuration of the wavelet transform means 32. In the present embodiment, orthogonal wavelet transform is performed in which each coefficient of the wavelet transform is orthogonal.

First, as shown in FIG. 13, the original image signal Sor
The wavelet transform unit 41 performs a wavelet transform on g. FIG. 14 is a block diagram showing the processing performed in the wavelet transform unit 41. As shown in FIG. 14, filtering is performed in the main scanning direction of the original image signal Sorg (signal LLk) using the basic wavelet functions H and G, and pixels in the main scanning direction are set to 1 pixel.
Each pixel is decimated (indicated by ↓ 2 in the figure), and the number of pixels in the main scanning direction is halved. Here, the function H is a high-pass filter, and the function G is a low-pass filter. further,
Filtering processing is performed on each of the signals obtained by thinning out the pixels in the sub-scanning direction using functions H and G, and the pixels in the sub-scanning direction are thinned out every other pixel to reduce the number of pixels in the sub-scanning direction by half. And a wavelet transform coefficient signal (hereinafter, also simply referred to as a signal) HH1, HL1,
LH1, LL1 (HHk + 1, HLk + 1, LHk +
1, LLk + 1). Here, the signal LL1 represents an image obtained by reducing the length and width of the original image by half, and the signals HL1 and L
H1 and HH1 represent vertical edge, horizontal edge, and oblique edge component images in a half reduced image of the original image, respectively.

Next, the signal LL1 is further subjected to a wavelet transform in the wavelet transform unit 41 to obtain signals HH2, HL2, LH2 and LL2.
Here, the signal LL2 represents an image obtained by reducing the length and width of the original image to １ ／, and the signals HL2, LH2 and HH2 represent the images of the vertical edge, the horizontal edge and the oblique edge component in the 縮小 reduced image of the original image, respectively. To represent.

Thereafter, the wavelet transform for the wavelet transform coefficient signal LLk obtained in each frequency band is repeated n times in the same manner as described above, so that the wavelet transform coefficient signals HH1 to HHn, HL1 to HL
n, LH1 to LHn and LL1 to LLn. here,
Wavelet transform coefficient signals HHn, HLn, LHn, LLn obtained by the n-th wavelet transform are
Compared to the original image signal Sorg, the number of pixels in each of the main and sub directions is (1 /
2) Since ⁿ is set, each wavelet transform coefficient signal has a lower frequency band as n is larger, and becomes data representing a low frequency component among the frequency components of the original image data. Therefore, the wavelet transform coefficient signal HHk (k = 0)
To n, the same applies hereinafter) representing changes in the frequency of the original image signal Sorg in both the main and sub directions, and the larger the value of k, the lower the frequency of the signal. The wavelet transform coefficient signal HLk represents a change in the frequency of the original image signal Sorg in the main scanning direction. The larger the value of k, the lower the frequency of the signal. Further, the wavelet transform coefficient signal LHk indicates a change in the frequency of the original image signal Sorg in the sub-scanning direction.

FIG. 15 shows a wavelet transform coefficient signal for each of a plurality of frequency bands. Note that FIG. 15 shows the state up to the state where the second wavelet transform is performed for convenience. Note that the signal LL2 in FIG.
Represents an image obtained by reducing the original image in each of the main and sub directions by ４.

The wavelet transform coefficient signal HH
Among the signals k, HLk, LHk, and LLk (k = 1 to n), the signals HHk, HLk, and LHk represent edge components in the frequency band. ), That is, the image contrast mainly in the frequency band. The wavelet transform coefficient signal LLk represents an image obtained by reducing the original image as described above. In the present embodiment, the wavelet transform coefficient signals HHk, HL
k and LHk are referred to as band-limited image signals, the wavelet transform coefficient signal LLk is referred to as a resolution signal, and the band-limited image signal and the resolution signal are collectively referred to as wavelet transform coefficient signals. Here, since the signal LLn of the lowest resolution is not necessary for the purpose of obtaining the band-limited image signal, the value is set to 0.

The conversion processing means 33 is provided in the first embodiment.
Performs frequency emphasis processing in the same manner as the conversion processing means 3 in
Things. FIG. 16 shows the configuration of the conversion processing means 33 as a way.
FIG. 3 is a schematic block diagram shown together with bullet conversion means 32.
You. As shown in FIG.
, The band-limited image signals HHk, HLk, L obtained in
Hk (hereinafter B_kIn the converter 51)
Different conversion function f for each frequency band₁~ F_nDepending on the desired size
The converted signal B _k'(H
Hk ', HLk', LHk ', k = 1 to n) are obtained.
You. Then, the converted signals HHk ', HLk', LH
k ′ is inversed by the inverse wavelet transform means 52.
Wavelet transform is performed. FIG. 17 shows an inverse wave
Inverse wavelet performed by the wavelet transform means 52
It is a figure for explaining conversion. As shown in FIG.
The converted signals HHn ', HLn', L of the lowest frequency band
Inverse wavelet transform for Hn ', LLn (= 0)
Processed by applying inverse wavelet transform by means 52
Only the signal LLn-1 'is obtained.

FIG. 18 is a block diagram showing the processing performed in the inverse wavelet transform means 52. As shown in FIG. 18, the converted signal LLn ′ (LLk ′, LLn = 0 when k = n) and the converted signal LHn ′ (LH
k ') in the sub-scanning direction, a process of providing an interval of one pixel between pixels (represented by # 2 in the figure), and inverse wavelet transform functions G', H 'corresponding to the functions G, H
Performs a filtering process in the sub-scanning direction and adds them, and furthermore, a process of spacing one pixel between pixels in the main scanning direction of a signal obtained by the addition (hereinafter referred to as a first added signal). At the same time, filtering is performed in the main scanning direction by the function G 'to obtain a first signal. On the other hand, signal HLn '(HLk') and signal HH
In the sub-scanning direction of n ′ (HHk ′), processing is performed to leave an interval of one pixel between pixels, and functions G ′, H ′
Performs a filtering process in the sub-scanning direction and adds them, and furthermore, a process of providing a one-pixel interval between pixels in the main scanning direction of a signal obtained by the addition (hereinafter referred to as a second added signal). At the same time, a filtering process is performed in the main scanning direction by the function H 'to obtain a second signal. Then, the first and second signals are added to obtain a converted signal LLn-1 '(LLk-1'). Since the lowest resolution wavelet transform coefficient signal LLn is set to 0, the transformed signal LLn-1 ′ is the original image signal Sor
It represents the bandpass characteristic of g.

Next, the converted signals HHn-1 ', HLn
-1 ', LHn-1', and LLn-1 'are subjected to inverse wavelet transform in the inverse wavelet transform means 52 in the same manner as described above to obtain a transformed signal LLn-2'. Then, in the same manner as above, the inverse wavelet transform is repeated up to the highest frequency band, and the transformed signals HH1 ', HL1', LL1 'are inverse-wavelet-transformed to obtain the high-frequency signal S1.

The obtained high-frequency signal S1 is subjected to a calculation as shown in equation (8) in the calculator 53 in the same manner as in the first embodiment, to obtain a processed image signal Sproc.

Next, the operation of the second embodiment will be described. FIG. 19 is a flowchart showing the operation of the second embodiment. First, an original image signal Sorg is read from a reading device or the like.
Is input to the image processing apparatus 1 (step S11). The original image signal Sorg is subjected to wavelet transform by the wavelet transform means 32 to obtain a wavelet transform coefficient signal for each frequency band (step S12).
Next, each wavelet transform coefficient signal B _k is transformed by the above-described transformation function to obtain a transformed signal B _k ′ (step S13). Thereafter, the converted signal Bk 'is
The inverse wavelet transform unit 52 performs inverse wavelet transform to obtain a high-frequency signal S1 (step S1).
4). Using the high frequency signal S1, the calculation represented by the above equation (8) is performed to obtain a processed image signal Sproc (step S15), and the process ends.

As described above, also in the second embodiment,
As in the conventional frequency emphasis processing method, the original image signal Sorg
The original image signal is _obtained by comparing the original image signal Sorg with the band-limited image signal Bk having the same number of pixels as the original image signal Sorg by using the original image signal. It is no longer necessary to perform an interpolation operation to make the number of pixels the same as the number of pixels. Therefore, the amount of calculation can be reduced, whereby image processing can be performed at high speed, and stress on the operator can be reduced.

Further, the degree of image processing of the processed image signal Sproc can be changed only by changing the value of the enhancement degree β (Sorg). Therefore, as described in the above-mentioned Japanese Patent Application Laid-Open No. 5-244508. , Compared to methods that modify the degree of image processing by modifying the nonlinear function and reconstructing the image,
The degree of image processing can be easily changed, whereby the calculation time for obtaining the processed image signal Sproc can be shortened, and the stress on the operator can be reduced.

In the first and second embodiments, inverse multi-resolution conversion is performed up to the highest frequency band to obtain a high-frequency signal S1 representing an image of the same resolution as represented by the original image signal Sorg. However, the inverse multi-resolution conversion may not be performed up to the highest frequency band, but may be performed up to an intermediate frequency band. In this case, by performing image processing on the original image signal Sorg based on the high-frequency signal obtained by performing the inverse multi-resolution conversion up to the intermediate frequency band, the frequency band included in the original image signal Sorg is obtained. Can be emphasized.

In the first and second embodiments, the frequency emphasis processing is performed as the image processing performed in the arithmetic units 25 and 53. However, the dynamic range compression processing may be performed. In this case, the high-frequency signal S1 obtained as described above is subjected to an operation as shown in the following equation (10), and the dynamic range of the original image signal Sorg is compressed. In the equation (10), the degree of dynamic range compression of the processed image signal Sproc can be changed by changing the value of D (Sorg-S1).

Sproc = Sorg + D (Sorg−S1) (10) (where, Sproc: processed image signal Sorg: original image signal D (Sorg−S1): dynamic range compression coefficient determined based on the low frequency component signal ( D is a function for converting Sorg-S1). In the case of performing dynamic range compression processing, examples of the function f _k for performing conversion processing on a band-limited image signal include conversion functions shown in FIGS. Alternatively, it is preferable to use a function combining these.

Here, the conversion function shown in FIG. 20 performs a conversion to suppress a band-limited image signal having a large amplitude, and determines the degree of suppression of a band-limited image signal having a high frequency band. This is made stronger than the low band-limited image signal in consideration of the fact that the amplitude of the high-frequency component contained in the edge of the actual radiation image is smaller than that of the low-frequency component. In an actual radiographic image, even a very steep edge does not have an accurate step shape, and the higher the frequency component, the smaller its amplitude in many cases. For this reason, it is desirable to suppress the band-limited image signal with a higher frequency from the smaller amplitude in accordance with the amplitude of each frequency component, and this can be realized by this function.

The function shown in FIG. 21 converts the band-limited image signal to a value determined based on the absolute value of the band-limited image signal and equal to or smaller than the absolute value. The smaller the absolute value of the converted image signal obtained when the band-limited image signal whose absolute value is within a predetermined range near 0 is converted, the smaller the value of the function is It is characterized by the following. In other words, each of these functions passes through the origin and the slope of the function is less than or equal to 1 irrespective of the value processed by the function, and the slope of the function near 0 is a function for processing the low frequency band. It is characterized by being smaller. When these functions add a signal obtained by integrating the converted image signal to the original image signal Sorg, the joint between the original image signal Sorg and the added signal, that is, the rise of the signal becomes more natural. This has the effect.

Further, the arithmetic units 25 and 53 may simultaneously perform the frequency emphasis processing and the dynamic range compression processing as shown in the following equation (11).
Note that in equation (11), β (Sorg) and D
By changing the value of (Sorg-S1), it is possible to change the degree of frequency enhancement processing and dynamic range compression of the processed image signal Sproc.

Sproc = Sorg + β (Sorg) · S1 + D (Sorg−S1 ′) (11) (where, Sproc: processed image signal Sorg: original image signal β (Sorg): enhancement coefficient determined based on the original image signal D (Sorg-S1): dynamic range compression coefficient determined based on low frequency component signal In this case, high frequency signal S1 for performing frequency emphasis processing
20 is obtained by performing a conversion process on the band-limited image signal by the conversion function shown in FIG. 9, and a high-frequency signal S1 ′ for performing the dynamic range compression process is obtained by performing the conversion process shown in FIG. 21 may be obtained by performing a conversion process using a conversion function shown in FIG.

As described above, when performing the frequency emphasizing process and the dynamic range compressing process, the number of calculations for performing the interpolating process is 22364160 × 3 = 67092480 times. Since this calculation amount is about 1.5 times smaller than the conventional calculation amount, the calculation time can be shortened by about 1.5 times.

Further, in each of the above embodiments, the band-limited image signal is subjected to the non-linear processing by using the conversion function as a non-linear function. However, the present invention is not limited to the non-linear processing. It may be.

[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 block diagram illustrating an outline of a band-limited image signal creation process.

FIG. 3 is a diagram schematically showing band-limited image signal creation processing.

FIG. 4 is a diagram showing an example of a filter used for a filtering process.

FIG. 5 is a diagram showing details of a low-resolution image signal creation process;

FIG. 6 is a diagram illustrating an example of a filter used for interpolation processing.

FIG. 7 is a schematic block diagram showing a configuration of a conversion processing unit together with a band-limited image signal generation unit;

FIG. 8 is a diagram schematically showing a conversion process;

FIG. 9 shows an example of a nonlinear function.

FIG. 10 is a flowchart showing the operation of the first embodiment.

FIG. 11 is a diagram schematically showing a conventional conversion process.

FIG. 12 is a schematic block diagram illustrating a configuration of an image processing apparatus according to a second embodiment of the present invention.

FIG. 13 is a schematic block diagram illustrating a configuration of a wavelet transform unit.

FIG. 14 is a block diagram showing processing performed in a wavelet transform unit;

FIG. 15 is a diagram illustrating a wavelet transform coefficient signal for each of a plurality of frequency bands.

FIG. 16 is a schematic block diagram showing a configuration of a conversion processing unit together with a band-limited image signal generation unit.

FIG. 17 is a diagram for explaining an inverse wavelet transform;

FIG. 18 is a block diagram showing processing performed in an inverse wavelet transform unit;

FIG. 19 is a flowchart showing the operation of the second embodiment.

FIG. 20 is a diagram illustrating an example of a nonlinear function when performing dynamic range compression processing (part 1);

FIG. 21 is a diagram illustrating an example of a non-linear function when performing dynamic range compression processing (part 2);

FIG. 22 is a diagram showing a main scanning direction and a sub-scanning direction of an original image.

[Explanation of symbols]

 Reference Signs List 1,31 Image processing device 2 Band-limited image signal creating means 3,33 Conversion processing means 10 Filtering processing means 11,23 Interpolation processing means 12 Subtractor 22,51 Transformer 24 Adder 25,53 Computing unit 32 Wavelet transform means 41 Wavelet transform unit 52 Inverse wavelet transform means

──────────────────────────────────────────────────の Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) H04N 1/41 H04N 5/202 5J064 5/202 5/205 5/205 1/40 101D F-term (Reference) 5B057 AA07 BA03 CA08 CA12 CA16 CB08 CB12 CB16 CD05 CD11 CE03 CE06 CG02 5C021 PA38 PA66 PA72 PA99 SA08 XA31 XB01 XB07 XB16 5C076 AA21 AA22 AA36 BA06 BA09 BB24 BB40 5C077 LL18 NN03 PP01 PP04 PP04 PP03 PP10 BC18 BC29 BD03

Claims (27)

[Claims] 1. An image processing method for performing an image process on an original image signal representing an original image based on a signal related to a high frequency component of the original image to obtain a processed image signal, comprising: Generating a signal based on a predetermined conversion function, obtaining a signal related to a high-frequency component of the original image from the band-limited image signal, and performing the image processing on the original image signal based on the signal related to the high-frequency component. An image processing method characterized by performing the above. 2. The image processing method according to claim 1, wherein an image of each frequency band represented by the band-limited image signal has a number of pixels corresponding to the frequency band. 3. The signal relating to the high frequency component has the same number of pixels as the original image signal.
Or the image processing method according to 2. 4. A multi-resolution conversion of the original image signal to generate the band-limited image signal, and performs a conversion process on the band-limited image signal based on the predetermined conversion function to convert the band-limited image signal. The image processing method according to any one of claims 1 to 3, wherein an image signal is obtained, and a signal related to the high-frequency component is obtained by performing inverse multi-resolution conversion of the conversion band limited image signal. 5. The image processing method according to claim 4, wherein the multi-resolution conversion is a conversion based on Laplacian pyramid decomposition or a wavelet conversion. 6. The image processing method according to claim 1, wherein a signal related to the high-frequency component is obtained from a band-limited image signal other than the band-limited image signal of the lowest frequency band. 7. The image processing method according to claim 1, wherein the predetermined conversion function is a non-linear function. 8. The image processing method according to claim 1, wherein the image processing is a frequency enhancement processing. 9. The image processing method according to claim 1, wherein the image processing is a dynamic range compression processing. 10. An original image signal representing an original image,
An image processing apparatus that performs image processing based on a signal related to a high-frequency component of the original image to obtain a processed image signal; a band-limited image signal creating unit that creates a band-limited image signal from the original image signal; A high-frequency component obtaining unit that obtains a signal related to a high-frequency component of the original image from the band-limited image signal, and an image processing unit that performs the image processing on the original image signal based on a signal related to the high-frequency component An image processing apparatus comprising: 11. The band-limited image signal creating means,
11. The image processing apparatus according to claim 10, wherein said band-limited image signal is generated by an image in each frequency band represented by said band-limited image signal so as to have a number of pixels corresponding to the frequency band. apparatus. 12. The apparatus according to claim 10, wherein said high frequency component acquiring means is means for acquiring a signal relating to said high frequency component such that a signal relating to said high frequency component has the same number of pixels as said original image signal. 12. The image processing device according to item 11. 13. The band-limited image signal creating means,
The band-limited image signal is created by performing multi-resolution conversion on the original image signal, and the high-frequency component acquiring unit performs a conversion process on the band-limited image signal based on the predetermined conversion function. 13. A means for obtaining a signal related to the high-frequency component by performing the conversion band-limited image signal by performing the conversion band-limited image signal and performing inverse multi-resolution conversion of the converted band-limited image signal. The image processing apparatus according to any one of the preceding claims. 14. The image processing apparatus according to claim 13, wherein the multi-resolution conversion is a conversion based on Laplacian pyramid decomposition or a wavelet conversion. 15. The apparatus according to claim 10, wherein said high-frequency component obtaining means is means for obtaining a signal relating to said high-frequency component from a band-limited image signal other than a band-limited image signal of a lowest frequency band. 2. The image processing device according to claim 1. 16. The image processing apparatus according to claim 10, wherein the predetermined conversion function is a non-linear function. 17. The image processing apparatus according to claim 10, wherein the image processing is a frequency emphasizing processing.
The image processing device according to the item. 18. The image processing apparatus according to claim 10, wherein the image processing is a dynamic range compression processing.
The image processing apparatus according to claim 1. 19. An original image signal representing an original image,
A computer-readable recording medium on which a program for causing a computer to execute an image processing method of performing an image processing based on a signal related to a high-frequency component of the original image to obtain a processed image signal, wherein the program includes the original image Generating a band-limited image signal from the signal; obtaining a signal related to a high-frequency component of the original image from the band-limited image signal based on a predetermined conversion function; Performing the image processing on an image signal. 20. The step of generating the band-limited image signal includes generating the band-limited image signal such that an image of each frequency band represented by the band-limited image signal has a number of pixels corresponding to the frequency band. 20. The computer-readable recording medium according to claim 19, wherein the steps are: 21. The step of obtaining a signal relating to the high-frequency component is a step of obtaining a signal relating to the high-frequency component such that the signal relating to the high-frequency component has the same number of pixels as the original image signal. 19 or 2
0. The computer-readable recording medium according to 0. 22. The step of creating the band-limited image signal is a step of creating the band-limited image signal by performing multi-resolution conversion of the original image signal. The step of obtaining a signal related to the high-frequency component includes: A procedure for performing a conversion process on the band-limited image signal based on the predetermined conversion function to obtain a converted band-limited image signal, and performing inverse multi-resolution conversion on the converted band-limited image signal,
22. The computer-readable recording medium according to claim 19, further comprising a step of obtaining a signal related to the high-frequency component. 23. The computer-readable recording medium according to claim 22, wherein the multi-resolution conversion is a conversion based on Laplacian pyramid decomposition or a wavelet conversion. 24. The method according to claim 19, wherein the step of obtaining the signal relating to the high frequency component is a step of obtaining a signal relating to the high frequency component from a band limited image signal other than the band limited image signal of the lowest frequency band. A computer-readable recording medium according to any one of the preceding claims. 25. The computer-readable recording medium according to claim 19, wherein the predetermined conversion function is a non-linear function. 26. The image processing method according to claim 19, wherein the image processing is a frequency enhancement processing.
A computer-readable recording medium according to claim 1. 27. The image processing apparatus according to claim 19, wherein the image processing is a dynamic range compression processing.
A computer-readable recording medium according to any one of the preceding claims.