JP2000268171A - Picture processing method and picture processor and record medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain the same frequency responding characteristics regardless of the pixel density of an original picture signal in a picture processing method for preparing plural picture signals each having different band limiting characteristics from an original picture signal, and for preparing plural converted picture signals by converting each picture signal based on plural conversion functions, and for obtaining an already processed picture signal to which processing such as the emphasis of the specific frequency components of the original picture signal is applied from each converted picture signal. SOLUTION: The pixel density information M of an original picture signal Sorg is inputted by a pixel density information inputting means 5, and the definition parameter of a conversion function in a conversion function defining means 4 is decided based on the pixel density information M. Then, processing is executed by a non-linear processing means 3 by using the function defined by the decided parameter.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to an image processing method and apparatus for performing 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. The present invention relates to a computer-readable recording medium.

[0002]

2. Description of the Related Art Heretofore, a number of techniques have been proposed by the present applicant to improve the diagnostic performance of radiation images by performing frequency enhancement processing or dynamic range compression processing using an unsharp mask image signal (hereinafter referred to as a blurred image signal). An image processing method and apparatus have been proposed (JP-A-55-163472,
No. 55-87953, JP-A-3-222577, JP-A-10-75395,
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) Japanese Patent Application Laid-Open No. H11-163873 proposes 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 a signal subjected to 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.
From the original image signal, create a plurality of band-limited image signals representing frequency components of a limited frequency band, and further convert the band-limited image signals to have desired sizes by different conversion functions, The addition 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) (where, 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, Japanese Patent Application Laid-Open No. 10-171983 discloses a frequency enhancement process. There has been proposed a method for preventing the occurrence of artifacts in a processed signal when the dynamic range compression processing is 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). And a signal relating to a low-frequency component (low-frequency component signal) is obtained, and a signal relating to the high-frequency component and a signal relating to the low-frequency component are added to the original image signal to perform the frequency emphasis processing and the dynamic range compression processing. It was done. This processing can be represented, for example, by the following equation (3).

Sproc = Sorg + β (Sorg) · Fusm (Sorg, Sus1, Sus2,... SusN) + D (Sorg−Fdrc (Sorg, Sus1, Sus2,... SusN)) (3) 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)} (where Sproc: processed image signal Sorg: original image signal Susk (k = 1~N): unsharp image signals f _uk (k = 1~N): transformation function f _dk (k = 1~N) used to obtain a high frequency component signal: low A conversion function β (Sorg) used to obtain a frequency component signal: an enhancement coefficient determined based on the original image signal D (Sorg−Fdrc): determined based on the low frequency component signal Dynamic range compression coefficient (D is a function for converting Sorg-Fdrc) In these frequency emphasizing processing and dynamic range compression processing (hereinafter referred to as conversion processing), a conversion function for converting a band-limited image signal is defined. By changing the frequency, the frequency response characteristic of the added signal to be added to the original image signal can be adjusted. For this reason, a processed image signal having desired frequency response characteristics such as prevention of generation of an artifact can be obtained depending on the definition of each conversion function. However, when actually trying to define a conversion function, it is easy to determine how to define each conversion function to obtain a desired result from the expressions such as the above (2) and (3). is not. For this reason, by specifying a desired frequency response characteristic of the processed image signal and determining the definition parameter of the conversion function based on the specified characteristic, the processed image signal having the desired frequency response characteristic can be simplified. (Japanese Patent Laid-Open No. 10-63838).

On the other hand, the blurred image signal used in the above-described conversion processing is thinned out by subjecting the pixels of the original image signal to predetermined filtering processing at predetermined intervals, and then the pixels corresponding to the thinned number are obtained. It is created by interpolating according to a predetermined interpolation method. As the filtering process, a process of removing a high-frequency component of an original image signal by a low-pass filter, specifically, a process of obtaining an average value of pixel values in the filter and a weighted average value is performed. In the filtering process performed to obtain a blurred image signal in Japanese Patent Application Laid-Open No. 10-75395, the filtering process is further performed on a signal having a small number of pixels obtained by performing a filtering process on an original image signal. Then, a plurality of different blurred image signals are created by interpolating the image signals having a small number of pixels obtained at each stage of the filtering so as to have the same number of pixels as the original image signal.

The blurred image signal is generated based on the original image signal as described above. The original image signal is read by a reading device at a predetermined reading density to obtain a predetermined pixel density. Is a digital signal that can be reproduced. In general, when a digitized image signal is reproduced as, for example, a print output, it is known that a frequency component equal to or lower than a certain frequency (Nyquist frequency) determined by the pixel density is reproduced correctly. That is, the reading density, that is, the pixel density is determined in consideration of the image quality level required at the time of reproduction, and is not always constant.

For example, in a radiation image reading / reproducing system, a radiation image of a human body recorded on a stimulable phosphor sheet is read as a digital image signal by laser beam scanning. May be different depending on the size of the data, or can be changed to an arbitrary value by a user setting.

Here, when filtering processing using the same low-pass filter and interpolation processing using the same interpolation method are performed on image signals having different pixel densities, that is, image signals having different Nyquist frequencies, a band-limited image signal obtained is obtained. , Specifically, the frequency band of the band-limited image signal depends on the pixel density. Thus, for example, when one original image is read at two reading densities and an original image signal having two pixel densities is obtained, a band-limited image signal is obtained using the same blurred image signal, and a frequency emphasis process or Even if the dynamic range compression processing is performed, there is a problem that the emphasized frequency band or the compressed frequency band differs between the two types of original image signals.

For this reason, information on the pixel density of the original image signal is obtained, a filter coefficient is selected from a plurality of filter coefficient lists based on the information, and a filtering process is performed on the original image signal by a filter of the selected filter coefficient. Has been proposed to obtain a blurred image signal (Japanese Patent Laid-Open No. 10-63837). Here, for example, the band-limited image signals obtained by performing the filtering process with the same low-pass filter on the original image signals having the reading densities of 5 lines / mm and 6.7 lines / mm have different frequency bands. According to this method, each original image signal is subjected to a filtering process using a different low-pass filter (real space filter), so that the peak position of the frequency band of each band-limited image signal obtained from each original image signal can be read. Can be substantially matched without depending on the image density. If the frequency emphasizing process or the like is performed using the band-limited image signal having the aligned peak positions, the emphasizing characteristic can be made somewhat independent of the reading density.

[0011]

However, according to the method described in Japanese Patent Application Laid-Open No. H10-63837, the reading density is originally different, and the energy of the band-limited image represented by the band-limited image signal has That is, since the size of the band-limited image signal differs according to the pixel density, even if the filtering process using the real space filter is performed as described above, the peak position and the shape of the frequency band of each band-limited image signal are completely determined by the reference characteristic. There is a problem that it is difficult to perform the rough adjustment only because it is difficult (there is a slight difference in characteristics), and the same parameter (conversion function) is used for each band-limited image signal. Even if a process that emphasizes the band-limited image signal is performed, if the pixel density is different, the frequency response characteristics of the processed image signal will differ slightly depending on the pixel density. Not be the image of the same characteristics.

The present invention has been made in view of the above circumstances, and a desired conversion process, for example, a process for emphasizing a specific frequency component, can be always performed in the same manner regardless of the pixel density of an original image. An object of the present invention is to provide an image processing method and apparatus, and a computer-readable recording medium in which a program for causing a computer to execute the image processing method is recorded.

[0013]

According to the image processing method of the present invention, a plurality of image signals having different band limiting characteristics are created from an original image signal having a predetermined pixel density.
In an image processing method of performing a conversion process on each of the image signals based on a predetermined conversion function to obtain a converted image signal and obtaining a processed image signal from the converted image signal, the conversion function based on the pixel density Are defined, and the conversion function is defined.

Here, the "band limiting characteristic" refers to a frequency response characteristic of each of a plurality of frequency bands in the original image signal.

The "conversion processing" is specifically a frequency emphasis processing for emphasizing a specific frequency component included in the original image signal as shown in the above equation (2), or a frequency emphasis processing in the above equation (3). A dynamic range compression process that lowers the contrast in the high-density region or low-density region or in both the high-density region and the low-density region so as to narrow the difference between the maximum density and the minimum density of the original image, that is, the dynamic range, is exemplified. .

Further, the "predetermined pixel density" may be input by an operator. Information relating to the pixel density is added to the original image signal, and the information relating to the pixel density is automatically processed when the original image signal is processed. May be obtained. The “pixel density” refers to not only the reading density at the time of reading a radiation image recorded on a stimulable phosphor sheet, for example, but also the resolution representing the relationship between the size of the original image and the sampling at the time of obtaining the original image signal. (For example, dpi).

Further, "determining the definition parameter of the conversion function based on a predetermined pixel density and defining the conversion function" means that the frequency response characteristic of the finally obtained processed image signal is To be unaffected by density,
This means that a conversion function is defined based on the pixel density. When determining the definition parameters, for example, the relationship between the plurality of image signals, the desired frequency response characteristics, and the definition parameters of the conversion function, the plurality of image signals and the frequency response characteristics are known values, A method such as finding a parameter by solving it as a simultaneous equation with the definition parameter as a variable, or a method of defining each conversion function by gradually adjusting the parameters defining the conversion function while observing the processed image Alternatively, a definition parameter is prepared for each of at least two or more pixel densities that are set in advance. By selecting the definition parameter corresponding to the close pixel density, the definition parameter of the conversion function is determined and the conversion function is determined. And a method of definition may also be used. As the conversion function, various functions such as a linear function, a nonlinear function, and a constant can be used.

In the image processing method according to the present invention, it is desirable to store the determined definition parameters in association with the original image signal.

Here, "storing the determined definition parameters in association with the original image signal" means that when the image is re-output, a processed image signal using the previously determined definition parameters can be used. This means that when the original image signal is stored after the conversion processing, the determined definition parameters and the original image are stored in association with each other. As long as the association is established, the definition parameters may be stored in the same storage device (storage medium) together with the original image signal, or both may be stored in separate storage devices.

In the image processing method according to the present invention, the original image signal is subjected to a filtering process using a filter having a filter coefficient determined based on the pixel density, so that a plurality of blurs having different frequency response characteristics are obtained. Creating an image signal, creating a plurality of band-limited image signals representing signals for each of a plurality of frequency bands of the original image signal, based on the original image signal and the plurality of blurred image signals, Preferably, the image signal is a plurality of image signals.

Here, the "blurred image signal" is an image signal representing an image having the same number of pixels as the original image signal but lower in sharpness than the original image signal. The blurred image signal is first thinned out by performing a predetermined filtering process at predetermined intervals on the pixels of the original image signal, and the same filtering process is repeatedly performed on the image signal thus obtained. A plurality of image signals with a reduced number of pixels are created, and each of them is created by performing an interpolation process by a predetermined interpolation method so that the number of pixels is the same as that of the original image. Here, the filtering process is performed by a filter having a filter coefficient determined based on the pixel density, and a specific method is described in the above-mentioned Japanese Patent Application Laid-Open No. 10-63837.

The "plurality of band-limited image signals representing signals of a plurality of frequency bands of the original image signal" may be created by taking a difference between blurred image signals of adjacent frequency bands, for example. It may be created by taking the difference between the original image signal and each blurred image signal. Alternatively, it can be created by taking a difference with another combination of the original image signal and the blurred image signal.

An image processing apparatus according to the present invention comprises: image signal creating means for creating a plurality of image signals having different band limiting characteristics from an original image signal having a predetermined pixel density; And a conversion means for performing a conversion process based on the conversion function to obtain a converted image signal and obtaining a processed image signal from the converted image signal, wherein the conversion function is defined based on the pixel density. A conversion function defining means for determining a parameter and defining the conversion function is provided.

In the image processing apparatus according to the present invention, the conversion function defining means has a table storing definition parameters corresponding to at least two or more predetermined pixel densities, and the definition parameter stored in this table is stored. The definition parameter may be determined by reading out the definition parameter corresponding to the pixel density closest to the predetermined pixel density among the two or more pixel densities set in advance.

It is desirable that the image processing apparatus according to the present invention includes a storage unit for storing the determined definition parameters in association with the original image signal.

The image signal creating means performs a filtering process on the original image signal with a filter having a filter coefficient determined based on the pixel density, and a plurality of blurred image signals having different frequency response characteristics from each other. A plurality of band-limited image signals representing signals of a plurality of frequency bands of the original image signal based on the original image signal and the plurality of blur image signals. It is preferable to include a band-limited image signal creating means for creating a signal.

Further, 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.

[0028]

According to the present invention, when a conversion process such as a frequency emphasis process is performed, the frequency response characteristic of the processed image signal is not influenced by the pixel density based on the pixel density of the original image signal. A conversion function definition parameter used in the processing is obtained, and the conversion processing is performed based on the conversion function defined by the parameter. For this reason, even if the reading density differs, the frequency response characteristics of the processed images (images subjected to enhancement processing and the like) with respect to a plurality of original image signals having different band limiting characteristics can be matched. It is possible to obtain a processed image signal capable of reproducing an image having a constant frequency response characteristic that is not affected by the pixel density of the image signal.

Further, a definition parameter is prepared for each of at least two or more pixel densities set in advance, and a definition parameter of a pixel density corresponding to a pixel density closest to a predetermined pixel density is selected from the prepared definition parameters. By doing so, it is possible to easily determine the definition parameters of the conversion function.

Further, if the determined definition parameters are stored in association with the original image, when the image is re-output to a film or a monitor, the processed image signal is processed using the definition parameters used for the previously output image. Therefore, an image having the same image quality as the previous image can be obtained.

[0031]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an embodiment of an image processing method and apparatus according to the present invention will be described 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 present embodiment. The image processing apparatus 1 includes a blurred image signal generating unit 2 that generates a blurred image signal from an original image signal Sorg having a predetermined pixel density obtained by a reading device and the like, and a frequency emphasis process for emphasizing a specific frequency. And a non-linear processing means 3 for obtaining a processed image signal Sproc. Further, the image processing apparatus 1
Comprises a conversion function definition means 4 and a pixel density information input means 5. The conversion function definition means 4 is a means for defining a conversion function used by the non-linear processing means 3 for the conversion processing. For example, parameters such as the slope of the function and the degree of non-linearity are input to the pixel density information input means 5. A conversion function is determined based on the information M. The pixel density information input means 5 is a means for obtaining the pixel density information M of the original image signal Sorg. The input by the pixel density information input unit 5 may be input by the user as a numerical value from a keyboard, or by displaying several types of densities on the operation screen and allowing the user to select. Alternatively, the reading device may attach the pixel density information M to the original image signal Sorg, and the image processing device 1 may recognize the information M attached to each input image signal. Any form may be used as long as the definition unit 4 can recognize the pixel density.

Here, the process of creating a blurred image signal will be described in detail. FIG. 2 is a block diagram showing an outline of the blurred image signal creation processing. As shown in FIG. 2, the blurred image signal creating means 2 of FIG. 1 first performs filtering processing on the original image signal Sorg in the x direction and the y direction of the pixels of the original image by the filtering processing means 10. To generate an image signal B ₁ (hereinafter referred to as a low-resolution image signal) having a lower resolution than the original image signal Sorg, and then perform the same filtering processing on the low-resolution image signal B ₁ to perform the low-resolution image signal processing. create a low-resolution image signal B ₂ further has lower resolution than the signal B _1, those to superimpose successive similar filtering process later. Then, the interpolation processing unit 11, the low-resolution image signal B _k obtained at each stage of this filtering process, respectively subjected to interpolation enlargement processing, the sharpness of different blurred image signal SUS1 to SUSn (hereinafter Susk (K = 1 ~
N).

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 (4) regarding the Gaussian signal.

[0035]

(Equation 1) This is because the Gaussian signal has good localization in both the frequency space and the real space. For example, a 7 × 1 one-dimensional filter when σ = 1 in the above equation (4) is shown in FIG. It is as shown in

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. By performing such a filtering process for every other pixel in the x direction and the y direction, the number of pixels of the low resolution image signal B ₁ becomes ４ of the original image, and the low resolution image signal obtained by the filtering process is reduced. By repeatedly performing this filtering process, n low-resolution image signals B _k (k =
1 to n) each have a pixel number of 1 of the original image signal Sorg.
/ ^22k image signal.

Next, a description will be given interpolation enlargement process performed on the low-resolution image signal B _k obtained in this manner. As the method of the interpolation operation, various methods such as a method using a B-spline can be cited. In the present embodiment, a Gaussian signal is also used for the interpolation operation because a low-pass filter based on a Gaussian signal is used in the filtering processing. Shall be. Specifically, in equation (5) below, a value approximated to σ = 2 ^k−1 is used.

[0038]

(Equation 2) When interpolating the image signals B ₁ represents, for a k = 1 σ =
It becomes 1. In this case, the filter for performing the interpolation processing is a 5 × 1 one-dimensional filter as shown in FIG.
The interpolation process, first the low resolution image signal B ₁ 1
Expanding a low-resolution image signal B ₁ in the original image and the same size by the value in every other pixel is interpolated by one pixel of 0, then above for the interpolated low resolution image signal B ₁ The filtering is performed by the one-dimensional filter shown in FIG.

Similarly, this interpolation enlargement processing is performed on all the low-resolution image signals _Bk . When interpolating the low-resolution image signal B _k , 3 × 2 ^k −
By creating a filter having a length of 1 and interpolating 2 ^k -1 pixels each having a value of 0 between each pixel of the image signal B _k , the pixel is enlarged to the same size as the original image. Are subjected to a filtering process with a filter having a length of 3 × 2 ^k −1 on the image signal B _k in which the pixels have been interpolated.

Here, in the frequency emphasizing process, it is assumed that the pixel density at the time when the original image signal Sorg is obtained is always constant, and when creating the processed image signal Sproc, this pixel density Was not considered at all. However, in an actual image processing system, since various image signals are input, not all image signals have the same pixel density. For example, in the radiation image processing system according to the present embodiment, the pixel density differs depending on the size of the stimulable phosphor sheet, and is 5 / mm in half-cut / large-angle size, and in 4-cut size.
It is 6.7 lines / mm, and 10 lines / mm in a 6-cut size. Further, the pixel density can be arbitrarily set by the user.

However, when image signals having different pixel densities, that is, image signals having different Nyquist frequencies are subjected to the filtering process using the same low-pass filter and the interpolation process using the same interpolation method, the blurred image will be described later. The frequency response characteristics of the band-limited image signal obtained from the signal Susk differ depending on the pixel density. For example, the reading density is 10 lines / mm,
A band-limited image signal is obtained from a blur image signal Susk obtained by performing a filtering process using a one-dimensional filter shown in FIG. 3A on each of the original image signals of 5 lines / mm and 6.7 lines / mm. If created, pixel density 10 lines / m
In the case of m and 5 lines / mm, the Nyquist frequency is 1
/ 2, the frequency bands are equal, but when the pixel density is 6.7 lines / mm, the pixel density is 10
This is completely different from the case of 5 lines / mm and 5 lines / mm.
For this reason, in the present embodiment, as shown in the method described in the above-mentioned Japanese Patent Application Laid-Open No. 10-63837, when the pixel density is 10 lines / mm and 5 lines / mm, the filter shown in FIG. In the case of a density of 6.7 lines / mm, filtering is performed using the filter shown in FIG. 3B so that the frequency band of the band-limited image signal obtained at any pixel density is substantially the same. ing. Note that the filter switching in the blurred image signal creating means 2 is performed based on the pixel density information M input from the pixel density information input means 5.

Next, the non-linear processing performed using the blurred image signal Susk generated as described above will be described. FIG. 6 is a schematic block diagram showing a configuration of an apparatus for performing a frequency emphasizing process as an example of the non-linear process together with the blurred image signal creating means 2. As shown in FIG. 6, a subtractor 21 calculates a difference between the original image signal Sorg and the blurred image signal Susk created by the filtering processing unit 10 and the interpolation processing unit 11, and calculates a difference between the signals. Band limited image signals (Sorg-Sus1, Sus1-Sus2, etc.), which are components of a limited frequency band, are created.

Then, the band-limited image signal is converted by the converter 2
2 are suppressed so as to have a desired size by different conversion functions f _{1 to} f _N , respectively, and the following equation (2)
Accordingly, the plurality of suppressed band-limited image signals are integrated in the arithmetic unit 23 and further added to the original image signal to generate a processed image signal Sproc. As a result, it is possible to obtain a processed image signal Sproc in which a desired frequency component is emphasized to a degree according to the purpose.

[0044] 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)... (2) (where, Sproc: an image signal in which high-frequency components are emphasized Sorg: an original image signal Susk (k = 1 to N) : Blurred image signal 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) Here, the conversion function f _k is: A non-linear function expressed by the following equation (6) is used.

F (Sin) = Sout = Sin × Y × (exp (X / Sin) −1) / (exp (X / Sin) +1) (6) In equation (6), Sin is an input signal, Sout is an output signal, X is a parameter for determining the degree of nonlinearity, and Y is a parameter for controlling the slope of the entire function. By adjusting the parameters X and Y, the frequency response characteristics of the band-limited image signal can be changed.

The generation of the processed image signal Sproc has been described above. Next, problems to be solved by the present invention and means for solving the problems will be described with reference to examples. By filtering the original image signal Sorg using a different filter for each pixel density as in the method described in the above-mentioned Japanese Patent Application Laid-Open No. 10-63837, the blur image signal Susk
If the band-limited image signal is obtained from the blurred image signal Susk, the band-limited image signal can be in the same frequency band regardless of the pixel density. However,
Although the same frequency band can be set, the frequency response characteristics of the band-limited image signal differ depending on the pixel density.

Here, the reading density is 10 lines / mm and 5 lines / m
The frequency response characteristics of the band-limited image signal created by performing the filtering process using the one-dimensional filter shown in FIGS. FIG. 7 shows the case where the reading density is 10 lines / mm.
FIG. 8 shows the case of 6.7 lines / mm, and FIG. 9 shows the case of 5 lines / mm. As shown in FIGS. 7 to 9, the highest frequency band of the band-limited image signal is determined by the pixel density, and the peak frequency (cycle / mm) of the band-limited image signal of the other frequency band is determined by each pixel density. In 1 (only 10 / mm), 0.5, 0.25,
As in 0.125, 0.06, and 0.03 (only 6.7 lines / mm and 5 lines / mm), the peak frequency of the band-limited image signal of the k-th stage is the peak frequency of the band-limited image signal of the (k-1) -th stage. It becomes 1/2 of the peak frequency. On the other hand, even if each band-limited image signal has substantially the same peak frequency, the magnitude of the response at each peak frequency, that is, the energy of the band-limited image signal and the shape of the response are slightly different for each pixel density. It has become. Therefore, if the same parameters X and Y are used regardless of the pixel density in the conversion function shown in Expression (6), the frequency response characteristics of the processed image signal Sproc will be slightly different for each pixel density. .

Therefore, in the present invention, the parameters X and Y of the conversion function are set in the conversion function definition means 4 based on the information M on the pixel density of the original image signal Sorg input from the pixel density information input means 5. The band-limited image signal is converted by a conversion function having a different parameter for each pixel density. Conversion function definition means 4
FIG. 10 shows a detailed configuration of the first embodiment. As shown in FIG. 10, the conversion function defining means 4 comprises a parameter setting means 7 and a memory 8 storing a set of parameters X and Y for each pixel density.

An example of a set of parameters X and Y is shown in Table 1 below. The set of parameters X and Y shown in Table 1 is based on a pixel density of 10 lines / mm, and the pixel density is 6.7 lines / mm and 5 pixels / mm.
Image signal S processed by parameters X and Y
When the proc is obtained, the frequency response characteristics are set to be substantially the same as the processed image signal Sproc obtained by the parameters X and Y of 10 lines / mm. In Table 1, the uppermost row corresponds to the band-limited image signal up to the Nyquist frequency, and the lowermost row represents 0.06 cycle / mm.
For a band-limited image signal having a peak at
The upper row is a parameter for a band-limited image signal in a high frequency band. Further, in the present embodiment, since a band-limited image signal having a peak at 0.03 cycle / mm cannot be obtained for a pixel read at a pixel density of 10 pixels / mm as a reference, 6.7 pixels / mm and 5 pixels / 3 obtained at a pixel density of / 3
The set of parameters is set to the highest frequency band and five sets from 0.5 to 0.06 so that the enhancement processing is not performed on the band-limited image signal having a peak at cycle / mm. 0.06 cycle / mm regardless of density
The emphasis processing is performed using the band-limited image signal between the band-limited image signal having the peak at the Nyquist frequency and the band-limited image signal.

[0050]

[Table 1] Then, the parameter selecting means 7 selects a set of parameters stored in the memory 8 based on the pixel density information M input from the pixel density information input means 5, and the selected set of parameters is stored in the conversion function defining means 4. To define a conversion function, and based on the defined conversion function, the nonlinear processing means 3 performs the frequency emphasis processing on the band-limited image signal based on the above equation (2).

FIGS. 11 to 13 show frequency response characteristics for each pixel density based on the conversion function set by the parameters X and Y. As shown in FIGS. 11 to 13, when the frequency enhancement processing is performed based on the conversion functions set by the parameters X and Y shown in Table 1, the frequency response characteristics become substantially the same regardless of the pixel density.

The parameters X and Y may be set, for example, by the method described in the above-mentioned Japanese Patent Application Laid-Open No. 10-63838. The setting method will be described below. An apparatus for setting the parameters X and Y prompts a user to input a frequency response characteristic via a display device such as a display, and recognizes a desired frequency response characteristic from the input. Specifically, it means software, an input device, a display device, and the like for performing such processing.

In the apparatus for setting the parameters X and Y, an operation screen as shown in FIG. 14 is displayed on the display. At this time, a frequency response characteristic curve of a signal obtained when the band-limited image signal is converted based on the conversion function (initial value) set at that time is displayed on the screen ((FIG. 14) 1)). Six movable indication points are displayed on the characteristic curve. That is, if the image processing is to generate n band-limited image signals and perform nonlinear processing, it is assumed that n designated points are displayed.

The user moves each designated point to a point corresponding to a desired frequency response using a pointing device such as a mouse (FIG. 14 (2)). This movement is performed so that the response in each frequency band becomes the same regardless of the pixel density. However, if the frequency response as displayed is sufficient, it is not always necessary to move. Points after the move operation (including points that were not moved)
Is recognized as the frequency indicated by the point and the desired frequency response at that frequency, the frequency response characteristic curve is recalculated based on this, and a new characteristic curve passing through the point after the movement operation is displayed on the screen (3 in FIG. 14). If the newly displayed characteristic curve is a desired one on the confirmation screen, the user instructs parameter setting. If not, the user can perform the operation of moving the indicated point again (see FIG. 14). (4)). The parameter setting is not limited to such a form.
For example, various other forms are conceivable, such as a form in which several frequencies are sequentially displayed on a screen, and a user sequentially inputs desired frequency response characteristics for the frequencies as numerical values.

When the parameter setting is instructed, for example, when processing is performed using six band-limited image signals, the input desired frequency response characteristic is calculated by the following simultaneous equation (7) a1 = Y1 x S11 + Y2 x S21 + Y3 x S31 + Y4 x S41 + Y5 x S51 + Y6 x S61 a2 = Y1 x S12 + Y2 x S22 + Y3 x S32 + Y4 x S42 + Y5 x S52 + Y6 x S62 a3 = Y1 x S13 + Y2 x S43 + Y3 x S3 + Y3 x S3 + Y3 x S4 + Y3 x S4 + Y3 x S4 + Y3 x S4 + Y3 x S4 + Y3 x S4 + Y3 x S4 + Y3 x S3 + Y3 x S4 + Y3 x S4 + Y3 x S4 + Y3 x S4 .Times.S34 + Y4.times.S44 + Y5.times.S54 + Y6.times.S64 a5 = Y1.times.S15 + Y2.times.S25 + Y3.times.S35 + Y4.times.S45 + Y5.times.S55 + Y6.times.S65 a6 = Y1.times.S16 + Y2.times.S26 + Y3.times.S36 + Y4.times.S46 + Y5.times.S6 + Y6.times.S6 + Y6. a1 to a6: Frequency response specified as desired value S11 to S66: Frequency response characteristic of band-limited image signal created based on original image signal Y1 to Y6: Parameter representing slope of conversion function near 0) , A1 ~ Fits as a6. However, the dimension of the equation is determined based on the number of band-limited image signals, that is, the number of designated values, and the above simultaneous equations are merely examples.

Then, the simultaneous equations shown in the equation (7) are converted into the designated frequency response characteristics a1 to a6 and the band-limited image signal.
The parameters Y1 to Y6 are obtained by solving S11 to S66 as default values and the parameters Y1 to Y6 as variables, thereby setting the parameters defining the conversion function. Note that the parameter X representing the degree of nonlinearity is a parameter Y
Any value may be set corresponding to 1 to Y6, but a constant value may be used for each pixel density as shown in Table 1.
Then, a set of parameters X and Y set for each pixel density is stored in the memory 8.

In the above equation (7), the values expressed as the frequency response characteristics S11 to S66 of the band-limited image signal are such that S11 to S16 (S21 to S26,. These correspond to the response characteristics of the predetermined six frequencies of the restricted image signal, and these correspond to the filter coefficient of the filter used in the blurred image signal creation and the interpolation coefficient.
It can be obtained by a conversion method generally used in frequency analysis, such as Fourier transform.

Next, the operation of this embodiment will be described. FIG. 15 is a flowchart showing the operation of the present embodiment. First, an original image signal Sorg is input to the image processing device 1 from a reading device or the like (step S1). The original image signal Sorg is input to the blurred image signal creating means 2, where the blurred image signal Susk is created according to the pixel density of the original image signal Sorg (step S2). On the other hand, pixel density information M of the original image signal Sorg is input to the pixel density information input means 5 (step S3), and based on the input pixel density information M, the pixel density information Is selected, and a conversion function is defined by the selected set of parameters (step S
4). It should be noted that the processing in step S1 is more than the processing in step S1
Although the processes 3 and 4 may be performed first, the processing time can be reduced by performing these processes in parallel. In the nonlinear processing means 3, the blurred image signal Susk
, A non-linear processing (frequency emphasis processing) represented by the above equation (2) is performed based on the conversion function defined by the conversion function definition means 4, and a processed image signal Sproc is obtained. (Step S
5).

This frequency emphasis processing is to generate an addition signal to be added to the original image signal for emphasis using the blurred image signal. In order to prevent an artifact from occurring by performing the frequency emphasis processing. Then, different conversion processing is performed for each frequency band so that the signals of each frequency band constituting the addition signal become desired signals. In order to create a desired signal, the signal preferably has a desired frequency response characteristic regardless of the pixel density of the original image signal Sorg. According to the present embodiment, since the parameters of the conversion function are defined so that the frequency response characteristics of the processed image signal Sproc are substantially the same regardless of the pixel density, regardless of the pixel density of the original image signal Sorg. First, a processed image signal Sproc having substantially the same frequency response characteristics can be obtained.

In the above description, the parameters X and Y are
Although the method is described as being set according to the method described in No. 10-63838, the present invention is not limited to this, and may be set by trial and error.

The definition of the basic resolution (a, b, c; a <b <c: c is the highest resolution, a = 5, b = 6.7, c = 10 in the above example) as shown in Table 1 above. A parameter table is prepared, and the resolution R of the original image to be processed
When s is different from the basic resolution (a, b, c; a <b <c: c is the highest resolution), among the basic resolutions a, b, c,
The definition parameter of the closest basic resolution may be selected according to the following rules.

When Rs> b + (c−b) / 2, the parameter for c b + (c−b) / 2 ≧ Rs> a + (ba) / 2, the parameter for b b + (c− b) Parameter for a when / 2 ≧ Rs This method makes it easy to determine the definition parameter of the conversion function.

It should be noted that a storage device may be provided to store the original image signal Sorg after the processing is completed. In this case, however, the definition parameter is determined regardless of the method of determining the definition parameter. The definition parameter, the basic resolution information and the image resolution information may be stored in the storage device in association with the original image as supplementary information of the original image signal Sorg. The storage destination of the supplementary information is, for example, the original image signal Sor.
It may be the same storage device that stores g, or may be another storage device. Thereby, when the image is re-output to the film or the monitor, the definition parameter corresponding to the original image signal Sorg of the previously output image is read from the storage device, and the processed image signal proc is generated using the read definition parameter. Thus, an image having the same image quality as the previously output image can be obtained. Further, since the process of determining the definition parameters can be omitted, an image is output in a short time.

In the above embodiment, the frequency emphasis processing shown in the above equation (2) is performed as the non-linear processing. However, the frequency emphasis processing and the dynamic range compression processing may be performed simultaneously. Hereinafter, an apparatus for performing this processing will be described. FIG. 16 is a schematic block diagram showing a configuration of an apparatus for performing a frequency emphasis process and a dynamic range compression process as an example of the non-linear process together with the blurred image signal creating means 2. As shown in FIG. 16, a difference between the original image signal Sorg and the blurred image signal Susk created by the filtering processing unit 10 and the interpolation and enlarging unit 11 is calculated by a subtracter 21, and Band limited image signals (Sorg-Sus1, Sus1-Sus2, etc.), which are components of a limited frequency band, are created. Here, the filter used in the filtering processing means 10 is determined according to the pixel density as in the above embodiment. The band-limited image signal obtained in this manner is, as shown in FIG.
And the second conversion means 3b, respectively, and are processed by the converters 22a and 22b of each conversion means.

The conversion by the converter 22a of the first conversion means 3a is performed using the conversion function defined based on the pixel density information M of the original image signal Sorg as described above. Here, the conversion in the converter 22a is performed using, for example, the conversion functions shown in FIGS. Note that these conversion functions are set so that the frequency response characteristics of the processed image signal Sproc are substantially the same regardless of the pixel density of the original image signal Sorg.
Parameters are set for each pixel density.

Here, the conversion function shown in FIG. 17 performs a conversion to suppress a band-limited image signal having a large amplitude, and determines the degree of suppression of the 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 of FIG. 18 converts a band-limited image signal to a value that is determined based on the absolute value of the band-limited image signal and is equal to or less 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.

Similarly, the converter 22 of the second converter 3b
The conversion by b is performed using, for example, the function shown in FIG. 19, the function of FIG. 18 described above, or a combination of the functions of FIG. 19 and FIG. Each of the conversion means 3a, 3b
The converted image signals output by
a and 23b. Here, the arithmetic unit 23a performs an operation for generating a signal necessary for the frequency emphasizing process, and the arithmetic unit 23b performs an operation for generating a signal necessary for the dynamic range compression process.

The arithmetic unit 23a performs the same frequency emphasizing processing as in the embodiment shown in FIG. The following processing is performed in the arithmetic unit 23a. First, as described above, the band-limited image signals converted by the first conversion unit 3a are integrated. When the integrated signal is obtained, the integrated signal is further multiplied by the enhancement degree β according to the value of the original image signal Sorg.

On the other hand, the arithmetic unit 23b performs a dynamic range compression process. The following processing is performed in the arithmetic unit 23b. First, as described above, the band-limited image signals converted by the second conversion unit 3b are integrated. Then, the obtained integrated signal is subtracted from the original image signal Sorg. Further, the difference signal created by the subtraction is converted by a conversion function to obtain a dynamic range compression coefficient.

The signals obtained by the arithmetic units 23a and 23b are respectively added to the original image signal Sorg by the adding means 28 to obtain a processed image signal Sproc.

The above processing is represented by the following equation (3).

Sproc = Sorg + β (Sorg) · Fusm (Sorg, Sus1, Sus2,... SusN) + D (Sorg−Fdrc (Sorg, Sus1, Sus2,... SusN)) (3) 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)} (where Sproc: processed image signal Sorg: original image signal Susk (k = 1~N): unsharp image signals f _uk (k = 1~N): transformation function f _dk used for the first conversion processing (k = 1~N): second A conversion function β (Sorg) used in the conversion processing of: an enhancement coefficient determined based on the original image signal D (Sorg-Fdrc): a dynamic lens determined based on the low-frequency component signal Compression coefficient (D is a function for converting Sorg-Fdrc) Here, in the embodiment shown in FIG. 16, a signal used for frequency emphasis processing and a signal used for dynamic range compression processing are respectively based on the original image signal Sorg. The original image signal Sorg
In this case, one of the frequency emphasis processing and the dynamic range compression processing is performed first, and the other signal is processed for the other signal. However, a low-density portion of a radiographic image is generally a portion where the imaging dose at the time of imaging is small, and therefore contains a relatively large amount of noise. On the other hand, in the frequency emphasizing process, since the density-dependent emphasizing process in which the degree of emphasis is higher in the higher density portion is performed, if the frequency emphasizing process is directly performed on the original image signal, the lower density portion is not emphasized. , Noise is not emphasized. On the other hand, if, for example, the dynamic range compression process is performed on the original image signal and then the frequency emphasis process is performed, the density of the low density portion is increased by the dynamic range compression process. That is, the part is emphasized in the emphasis processing, and at the same time, the noise included in the part is emphasized. Therefore, it is desirable to generate each signal based on the original image signal Sorg as in the embodiment shown in FIG.
Also, in terms of shortening the processing time, it is desirable that the two processes be performed in parallel.

As described above, even when the dynamic range compression processing is performed, the parameters of the conversion function are set so that the frequency response characteristics of the processed image signal Sproc become substantially the same regardless of the pixel density of the original image signal Sorg. By definition, the processed image signal Spr having substantially the same frequency response characteristics regardless of the pixel density of the original image signal Sorg
You can get oc.

In the above embodiment, 10 wires / m
If the parameters of the conversion function as shown in Table 2 below are set for a pixel density of m, the processed image signal Spr
The frequency response characteristic of oc is as shown in FIG. Here, as shown in FIGS. 7 to 9, at a pixel density of 10 lines / mm, there is a band-limited image signal corresponding to 1 cycle / mm, but at a pixel density of 6.7 lines / mm and 5 lines / mm, Since there is no band-limited image signal of 1 cycle / mm, the processed image signal Spr is processed over all frequency bands.
The frequency response characteristics of oc cannot be matched regardless of the pixel density. On the other hand, in an image obtained by reproducing the processed image signal Sproc, the low-frequency component is visually more noticeable than the high-frequency component. Therefore, in such a case, the band limitation at a pixel density of 6.7 lines / mm and 5 lines / mm is performed so that the high-frequency components match to some extent and the low-frequency components of 1 cycle / mm or less match the frequency response characteristics. For an image signal, a conversion function may be defined by parameters X and Y as shown in Table 2. 6.7 / mm and 5 / mm
21 and 22 show the frequency response characteristics of the processed image signal Sproc obtained by converting the band-limited image signal having the pixel density described above with the conversion function defined by the parameters shown in Table 2. As shown in FIGS. 21 and 22, when the band-limited image signal is converted by the conversion function defined by the parameters shown in Table 2, the degree of coincidence is low for high-frequency components, but 1 cycle / cycle that is visually conspicuous.
The frequency response characteristics of low-frequency components of less than mm are the same. Therefore, it is possible to obtain a processed image signal Sproc capable of reproducing an image whose frequency response characteristics match in a low-frequency component that is visually noticeable regardless of the pixel density.

[0076]

[Table 2] Further, in the above embodiment, the stimulable phosphor sheet is
Non-linear processing is performed on the original image signal Sorg obtained by reading at a pixel density of 10 lines / mm, 6.7 lines / mm and 5 lines / mm so that a constant frequency response characteristic is obtained regardless of the pixel density. The original image signal S
The org is not limited to the one read from the stimulable phosphor sheet. For example, the resolution of the image represented by the image signal such as the relationship between the size of the subject and the sampling interval (dpi) is known in advance. Then, the present invention can be applied to any image signal.

Further, in the above-described embodiment, 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. Is also good. In this case, a parameter or a constant value of a linear function is set according to the pixel density of the original image signal.

In the above embodiment, the blurred image signal is obtained from the original image signal by filtering and interpolation and enlargement, and the band-limited image signal is created from the original image signal and the blurred image signal. However, the present invention is not limited to this. For example, even if an original image signal is converted to multiple resolutions by a method such as wavelet transform or Laplacian pyramid, and a blurred image signal is created from the converted image signals for each resolution, a band-limited image signal is created. Good.

[Brief description of the drawings]

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

FIG. 2 is a schematic block diagram illustrating a configuration of a blurred image signal creating unit.

FIG. 3 is a diagram illustrating an example of a filter used in a filtering process;

FIG. 4 is a diagram showing details of a low-resolution image signal creation process;

FIG. 5 is a diagram illustrating an example of a filter used for interpolation enlargement processing;

FIG. 6 is a diagram illustrating an example of a frequency emphasis processing device.

FIG. 7 is a diagram showing a frequency response characteristic of a band-limited image signal (pixel density 10 lines / mm).

FIG. 8 is a diagram showing a frequency response characteristic of a band-limited image signal (pixel density: 6.7 lines / mm);

FIG. 9 is a diagram illustrating a frequency response characteristic of a band-limited image signal (pixel density: 5 lines / mm);

FIG. 10 is a schematic block diagram illustrating a configuration of a conversion function definition unit.

FIG. 11 is a diagram illustrating a frequency response characteristic of a processed image signal processed according to the present embodiment (pixel density 10 lines / m);
m)

FIG. 12 is a diagram illustrating a frequency response characteristic of a processed image signal processed according to the present embodiment (pixel density: 6.7 lines / m);
m)

FIG. 13 is a diagram illustrating a frequency response characteristic of a processed image signal processed according to the present embodiment (pixel density 5 lines / m);
m)

FIG. 14 is a diagram showing an example of a user interface for designating frequency response characteristics.

FIG. 15 is a flowchart showing the operation of the present embodiment.

FIG. 16 is a diagram illustrating an example of a dynamic range compression processing device.

FIG. 17 is a diagram illustrating an example of a conversion function in a first conversion unit.

FIG. 18 is a diagram illustrating another example of the conversion function in the first conversion unit.

FIG. 19 is a diagram illustrating an example of a conversion function in a second conversion unit.

FIG. 20 is a diagram illustrating a frequency response characteristic of a processed image signal processed according to the present embodiment (pixel density 10 lines / m);
m)

FIG. 21 is a diagram illustrating a frequency response characteristic of a processed image signal processed according to the present embodiment (pixel density: 6.7 lines / m);
m)

FIG. 22 is a diagram illustrating a frequency response characteristic of a processed image signal processed according to the present embodiment (pixel density 5 lines / m);
m)

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Image processing apparatus 2 Blur image signal creation means 3 Non-linear processing means 4 Conversion function definition means 5 Pixel density information input means 10 Filtering processing means 11 Interpolation processing means 21 Subtractor 22 Transformer 23 Computing unit

Claims (21)

[Claims] An image signal having different band limiting characteristics is created from an original image signal having a predetermined pixel density, and a conversion process is performed on each of the image signals based on a predetermined conversion function. An image processing method for obtaining a converted image signal and obtaining a processed image signal from the converted image signal, wherein a definition parameter of the conversion function is determined based on the predetermined pixel density, and the conversion function is defined. Image processing method. 2. A method for determining a definition parameter of the conversion function, comprising: defining a definition parameter for each of at least two or more preset pixel densities; 2. The image processing method according to claim 1, wherein the step is performed by selecting a definition parameter corresponding to a pixel density closest to the predetermined pixel density among the densities. 3. The image processing method according to claim 1, wherein the determined definition parameters are stored in association with the original image signal. 4. The original image signal is subjected to a filtering process using a filter having a filter coefficient determined based on the pixel density to generate a plurality of blurred image signals having different frequency response characteristics from each other. Creating a plurality of band-limited image signals representing signals for each of a plurality of frequency bands of the original image signal based on the signals and the plurality of blurred image signals, and using the plurality of band-limited image signals as the plurality of image signals The image processing method according to any one of claims 1 to 3, wherein: 5. The image processing method according to claim 1, wherein the predetermined conversion function is a non-linear function. 6. The image processing method according to claim 1, wherein the conversion process is a frequency emphasis process. 7. The image processing method according to claim 1, wherein the conversion processing is a dynamic range compression processing. 8. An image signal generating means for generating a plurality of image signals having band limiting characteristics different from each other from an original image signal having a predetermined pixel density, based on a predetermined conversion function for each of the image signals. A conversion means for performing a conversion process to obtain a converted image signal and obtaining a processed image signal from the converted image signal, wherein a definition parameter of the conversion function is determined based on the pixel density. An image processing apparatus comprising a conversion function defining means for defining a conversion function. 9. The conversion function defining means has a table storing definition parameters corresponding to at least two or more pixel densities set in advance, and among the definition parameters stored in the table, the preset function includes 9. The image according to claim 8, wherein the definition parameter is determined by reading a definition parameter corresponding to a pixel density closest to the predetermined pixel density among the two or more pixel densities obtained. Processing equipment. 10. The image processing apparatus according to claim 8, further comprising a storage unit configured to store the determined definition parameter in association with the original image signal. 11. The image signal generating means performs a filtering process on the original image signal with a filter having a filter coefficient determined based on the pixel density, and outputs a plurality of blurred image signals having different frequency response characteristics from each other. A plurality of band-limited image signals representing signals of a plurality of frequency bands of the original image signal based on the original image signal and the plurality of blur image signals. 9. A band-limited image signal creating means for creating a signal.
The image processing apparatus according to any one of claims 1 to 10. 12. The image processing apparatus according to claim 8, wherein the predetermined conversion function is a non-linear function. 13. The conversion processing in the conversion means,
8. The method according to claim 8, wherein the frequency emphasis processing is performed.
3. The image processing apparatus according to claim 1. 14. The conversion processing in the conversion means,
13. The image processing device according to claim 8, wherein the image processing is a dynamic range compression process. 15. A plurality of image signals having different band limiting characteristics from an original image signal having a predetermined pixel density, and performing a conversion process on each of the image signals based on a predetermined conversion function. A computer-readable recording medium storing a program for causing a computer to execute an image processing method for obtaining a converted image signal and obtaining a processed image signal from the converted image signal, wherein the program is based on the pixel density A computer-readable recording medium having a procedure for determining a definition parameter of a conversion function and defining the conversion function. 16. The step of determining the definition parameter of the conversion function and defining the conversion function includes preparing a definition parameter for each of at least two or more pixel densities set in advance, and 12. A procedure performed by selecting a definition parameter corresponding to a pixel density closest to the predetermined pixel density among the two or more predetermined pixel densities.
The computer-readable recording medium according to claim 1. 17. The computer-readable recording medium according to claim 11, further comprising a step of storing the determined definition parameter in association with the original image signal. 18. The method of producing a plurality of image signals, wherein the original image signal is subjected to a filtering process using a filter having a filter coefficient determined based on the pixel density, and the plurality of image signals have different frequency response characteristics. And generating a plurality of band-limited image signals representing signals for a plurality of frequency bands of the original image signal based on the original image signal and the plurality of blur image signals. 18. The computer-readable recording medium according to claim 15, wherein the recording medium is a signal. 12. The computer-readable recording medium according to item 11. 19. The method according to claim 15, wherein the predetermined conversion function is a non-linear function.
A computer-readable recording medium according to claim 1. 20. The computer-readable recording medium according to claim 15, wherein said conversion process is a frequency emphasis process. 21. The method according to claim 15, wherein the conversion processing is a dynamic range compression processing.
The computer-readable recording medium according to claim 1.