JPH09163146A - Image processing method and image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make grid lines unnoticeable in the image processing method and device in which fog mask processing is applied to an image including a grid image. SOLUTION: A grid image eliminating signal S' is obtained by eliminating a grid image from an original image signal Sorg by a grid image elimination means 1. A fog image signal generating means 2 generates a fog image signal Susk'(k=1-n) based on the grid image elimination signal S', and a band limit image signal generating means 3 generates a band limit image signal based on the fog image signal Susk'. A conversion means 4 applies conversion processing with a nonlinear function to the band limit image signal, and an adder means 5 adds the original image signal Sorg with a prescribed emphasis coefficient multiplied therewith to the converted band limit image signal to obtain a processed image signal Sproc in which a high frequency component of the original image is emphasized.

Description

Detailed Description of the Invention

[0001]

The present invention relates to an image signal,
The present invention relates to an image processing method and apparatus for performing a non-sharp mask process (so-called blur mask process) for enhancing a high-frequency component of the image signal.

[0002]

2. Description of the Related Art In various fields, an image signal representing an image is obtained, the image signal is subjected to appropriate image processing, and then the image is reproduced and displayed. For example, in order to improve the diagnostic performance of a radiation image, the present applicant has proposed a method of performing frequency enhancement processing such as non-sharp mask processing (hereinafter referred to as blur mask processing) on an image signal (Japanese Patent Application Laid-Open No. Sho. 55-163472, JP-A-55-87953, etc.). This frequency processing is performed by adding to the read original image signal Sorg a product obtained by subtracting a non-sharp mask image signal (hereinafter referred to as a blurred image signal) Sus from the original image signal Sorg and multiplying the product by the enhancement degree β. In this way, a predetermined spatial frequency component is emphasized in the image. This is expressed by the following equation (1).

Sproc = Sorg + β (Sorg) (Sorg−Sus) (1) (Sproc: frequency-processed signal, Sorg: original image signal, Sus: blurred image signal, β: enhancement degree) where the blurred image The signal Sus is the original image signal Sorg within the M × N range around each pixel, which constitutes the image.
With respect to, Sus = ΣSorg / (M × N) (2)

The blur mask processing as described above is performed to improve the image quality of an image, but has the following problems. That is, when a blurred image signal is created in the vicinity of an edge portion where the density is abruptly changed in the image, the edge portion is included in the M × N mask as shown in FIG. The signal is influenced by the density of the edge portion, and an artifact such as overshoot, undershoot, or false contour occurs in the image processed by the blurred image signal, which deteriorates the image quality.

Therefore, a plurality of non-sharp mask signals having different frequency response characteristics are created, and a band-limited image signal representing a signal for each of a plurality of frequency bands of the original image signal is created based on the non-sharp mask signals. , An image processing method and an apparatus for performing processing based on the following formula (3) have been proposed (Japanese Patent Application No. 7-252088).

Sproc = Sorg + β (Sorg) · Fusm (Sorg, Sus1, Sus2, ... SusN) Fusm (Sorg, Sus1, Sus2, ... SusN) = {fu1 (Sorg−Sus1) + fu2 (Sus1−Sus2) + ... + FuN (SusN-1 −SusN)} (3) where Sproc: image signal in which high frequency components are emphasized Sorg: original image signal Susk (k = 1 to N): non-sharp mask image signal fuk (k = 1 to N) ): Non-linear function for converting each band-limited image signal According to this method, it is possible to perform the blur mask processing for obtaining a good processed image without causing an artifact near the edge portion.

On the other hand, when photographing and recording a radiation image of a subject on a recording sheet such as an X-ray film or a stimulable phosphor sheet, about 4.0 lines / mm so that the radiation scattered by the subject is not applied to the recording sheet. In some cases, a grid is arranged between the subject and the recording sheet, in which a grid in which radiation that does not transmit radiation and that easily transmits radiation such as aluminum and wood are alternately arranged at a fine pitch. When the image is captured using the grid, the radiation scattered by the subject is less likely to irradiate the recording sheet, so the contrast of the radiation image of the subject can be improved, but on the other hand, the recording sheet has a fine striped pattern along with the subject image. A grid image is recorded.

On the other hand, a radiation image reading apparatus which obtains an image signal from a recording sheet on which a radiation image is recorded, usually photoelectrically reads light carrying radiation image information, and obtains the highest spatial frequency (hereinafter , And a sampling interval Δx = 1 / (2 · fss) corresponding to the required highest spatial frequency is represented by fss) to obtain a digital image signal. The image signal obtained in this way is not only useful information representing the radiation image of the object, but is caused by the grid image even if the spatial frequency of the grid image is higher than the required highest spatial frequency fss. It may also contain noise.

FIG. 19 is a graph showing a spatial frequency characteristic of a radiation image (including a grid image) recorded on a recording sheet in a direction orthogonal to the striped pattern of the grid image.

It is assumed here that imaging is performed using a grid of 4.0 lines / mm, the spatial frequency of the grid image is 4 cycles / mm, and the maximum spatial frequency fss required to reproduce the radiation image of the subject is 2.5 cycles / mm. It will be described as mm.

FIG. 20 shows a sampling interval Δx = corresponding to fss = 2.5 (cycle / mm) in order to obtain information on a spatial frequency band below fss = 2.5 (cycle / mm) necessary for reproducing a radiation image of a subject. 1 / (2 · fss) = 0.2 (mm), that is, a diagram for explaining a mixed state of noise when sampling is performed 5 times per 1 mm.

In this case, so-called aliasing noise is mixed in at the position where the graph shown by the solid line in the figure (the same as the graph in FIG. 19) is folded back at fss = 2.5 (cycle / mm). Therefore, in this case, the spatial frequency of the grid image is 4 cycl.
Aliasing of e / mm occurs at 1 cycle / mm.

FIG. 21 shows the sampling interval Δx = 1 / (2
-The spatial frequency characteristic of the radiographic image carried by the image signal obtained by sampling at fss) = 0.2 (mm) is shown.

Noise corresponding to the grid image is mixed in at a position of 1 cycle / mm, and when a visible image is reproduced and recorded, the visible image appears as a striped pattern of 1 cycle / mm.
When observing a radiation image, even if the grid image is in a spatial frequency band where it is not so conspicuous, a striped pattern appears in the spatial frequency band that is easily noticeable when sampling to obtain an image signal, and reproduction is performed based on this image signal. Sometimes the visible image was very hard to see.

Therefore, the applicant of the present invention applied a filtering process to the image signal by a grid removal filter for removing the grid image from the image containing the grid image to reduce or remove the stripe pattern noise caused by the grid. An image processing method for obtaining a visible image that is easy to observe has been proposed (JP-A-3-14039).

[0016]

However, when a grid image is included in the image subjected to the above-mentioned blur mask processing, when the high frequency component of the original image is emphasized, the grid image is emphasized and the image becomes difficult to see. I was sick.
In addition, when a grid image is included when processing is performed on the high-frequency component of the original image by a non-linear function for converting each band-limited image signal, the converted signal value becomes a grid when the signal value is near 0. It is difficult to control the non-linear processing because it is slightly touched due to the influence of the image, and an artifact is generated in the reproduced image because the signal value is touched.

In view of the above-mentioned circumstances, the present invention provides an image processing method which can easily process a high frequency component of an original image without emphasizing the grid image even if the original image includes a grid image. The purpose is to provide a device.

[0018]

An image processing method and apparatus according to the present invention captures an original image composed of a striped grid image and a radiation image corresponding to the grid by capturing an image using the grid. In an image processing method for emphasizing a high frequency component of the original image by adding a signal related to a high frequency component of the original image to the original image signal represented,
A process for removing the grid image from the original image signal to obtain a grid image removal signal, and a signal relating to a high frequency component of the original image is created based on the grid image removal signal. Is.

[0019]

The image processing method and apparatus according to the present invention performs a grid image removing process on an original image signal representing an original image including a grid image, and based on the signal from which the grid image is removed. The processing is performed by creating a high frequency component of the original image and adding a signal representing the high frequency component to the original image signal. Therefore, even if the original image contains a grid,
The grid image is not emphasized when the high-frequency component of the original image is emphasized, and an easy-to-see image in which the grid image is inconspicuous can be obtained. Further, even in the case where the band-limited image signal is nonlinearly processed as described in Japanese Patent Application No. 7-252088, the processed signal is not touched near the signal value 0, so The control of the non-linear processing in the equation (3) is also facilitated, and the degree of freedom of the non-linear processing can be increased. In addition, since the high frequency component from which the grid image has been removed is emphasized and this is added to the original image signal, the original image signal is The remaining signal components are not erased.

[0020]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a diagram showing the concept of an image processing apparatus according to the present invention. As shown in FIG. 1, the image processing apparatus according to the present invention includes a grid image removing means 1 for removing a grid image from an original image signal Sorg representing an input original image including a grid image to obtain a grid image removing signal S '. , A blurred image signal creating means 2 for creating a blurred image signal Sus 'based on the grid image removal signal S', and a band for creating a band limited image signal by subtracting the blurred image signal Sus 'from the grid image removal signal S'. A processed image signal obtained by adding the band-limited image signal creation unit 3, the conversion unit 4 that performs a predetermined conversion process to the band-limited image signal, and the band-limited image signal that has been subjected to the nonlinear process and the original image signal Sorg. And adding means 5 for obtaining Sproc.

First, the processing performed in the grid image removing means 1 will be described. In the grid image removing means 1, the original image signal Sorg is processed by the following 7 × 7 filter.
Is subjected to a filtering process to obtain a grid image removal signal S'representing an image from which the grid image has been removed.

[0023]

[Equation 1]

The frequency characteristic of this filter is shown in FIG.
As shown in FIG. 2, the frequency characteristic of this filter is 3.4.
No response at cycle / mm and 4.0 cycle / mm
It is something that has become. This is because normally used grids are 34 lines / mm or 40 lines / mm, and the response in this frequency band is zero. Therefore, the grid image removal signal S'obtained by performing the filtering process with this filter has the grid image removed.

Next, the processing performed in the blurred image signal creating means 2 will be described. Blurred image signal creating means 2
, A filter F substantially corresponding to a one-dimensional Gaussian distribution on a 5 × 1 grid as shown in FIG. 3 is used for the grid image removal signal S ′. This filter F has the following formula (4)

[0026]

(Equation 2)

In the above, σ = 1. Here, the reason why the Gaussian signal is used as the filter F is that the Gaussian signal has good localization in both the frequency space and the real space.

Then, the filtering process is performed by the filter F in the x direction and the y direction of the pixels of the original image, so that the entire grid image removal signal S'is filtered. As a result, the blurred image signal Sus' of the grid image removal signal S'is obtained.

In the above embodiment, the one-dimensional filter F shown in FIG.
Although the filtering process is applied to the direction, the original image signal Sorg and the filtering-processed image signal are subjected to the filtering process at a time by a 5 × 5 two-dimensional filter as shown in FIG. Is also good.

Next, in the band-limited image signal producing means 3, the blurred image signal Sus' is subtracted from the grid image removal signal S ', and the band-limited image signal (S'-Sus') is obtained.

Further, the conversion means 4 performs conversion processing on the band-limited image signal (S'-Sus ') according to the signal value of the band-limited image signal (S'-Sus'). This conversion process is performed by a function fu as shown in FIG. 5, for example. This function fu is a non-linear function that has a slope of 1 when the absolute value of the band-limited image signal is smaller than the threshold Th1 and has a slope smaller than 1 when it is larger than the threshold Th1.

The band-limited image signal (S'-Sus') converted by such a function fu is added by the adding means 5 described above.
In, the enhancement degree β corresponding to the image signal S ′ is multiplied and added with the original image signal Sorg to obtain the processed image signal Sproc.

The above processing is shown in the following equation (5).

Sproc = Sorg + β (S ′) (fu (S′−Sus ′)) (5) The image obtained by reproducing Sproc thus obtained has the grid image removed and Even though the high frequency components of the original image are emphasized, the grid image is not emphasized. That is, when the high frequency component of the original image is emphasized without removing the grid image, noise caused by the grid image becomes noticeable as shown in FIG. 6A, and the conversion means is influenced by the noise caused by the grid image. The signal value fluctuates near the signal value 0 of the function fu of the conversion process in 4 and the control of the conversion process is difficult. On the other hand, according to the image processing method of the present invention, since the high frequency component is emphasized after the grid image is removed, the emphasized signal value becomes as shown in FIG. Noise caused by the image is not noticeable. Further, since the signal value does not fluctuate in the vicinity of the signal value 0 of the conversion processing function fu in the conversion means 4, the conversion processing can be easily controlled. Further, since the high frequency component from which the grid image is removed is emphasized and added to the original image signal Sorg, the original image signal Sorg is compared with the one in which the high frequency component is added to the grid image removal signal S '. Remains, so
It does not erase the necessary signal components.

The image processing method and apparatus according to the present invention can be applied to the above-mentioned Japanese Patent Application No. 7-252088. An image processing method and apparatus according to the present invention will be described below.
An embodiment applied to No. 7-252088 will be described.

FIG. 7 shows an image processing method according to the present invention.
It is a figure showing the concept of the apparatus applied to No. 7-252088. FIG.
As shown in FIG. 3, the image processing apparatus according to the present invention includes a grid image removing means 1 for removing a grid image from an original image signal Sorg representing an input original image including a grid image to obtain a grid image removing signal S ', and a grid image removing means S'. Blurred image signal creating means 2 for creating blurred image signals Susk '(k = 1 to n) of different resolutions having different frequency response characteristics based on the image removal signal S', and created by the blurred image signal creating means 2. A band-limited image signal creating unit 3 that creates a plurality of band-limited image signals based on the blurred image signal Susk ′, and a plurality of band-limited image signals created by the band-limited image signal creating unit 3 that are higher than a predetermined threshold value. The conversion means 4 for converting the band-limited image signal having a large absolute value of the signal value to reduce the absolute value as described later and the converted band-limited image signal are added. And adding means 5 for creating a sum signal Te is multiplied by a predetermined emphasis coefficient to the addition signal the original image signal S
The blur mask processing means 6 obtains the processed image signal Sproc in which the high frequency component of the original image is emphasized by adding it to org.

First, in the grid image removing means 1, filtering processing is performed in the same manner as in the above-described embodiment, and a grid image removing signal S'representing an image from which the grid image is removed is obtained.

Next, the processing performed in the blurred image signal creating means 2 will be described.

FIG. 8 is a block diagram for explaining the processing performed in the blurred image signal generating means 2. As shown in FIG. 8, the grid image removal signal S ′ is filtered by the low-pass filter in the filtering processing means 10. As this low-pass filter, for example, the filter F substantially corresponding to the one-dimensional Gaussian distribution on the 5 × 1 grid as shown in FIG. 3 is used. Then, the filtering process is performed by the filter F in the x direction and the y direction of the pixels of the original image, so that the entire original image signal Sorg is filtered. FIG. 9 is a diagram showing details of the filtering process. As shown in FIG. 9, the filter F shown in FIG. 3 performs a filtering process on every other pixel of the grid image removal signal S ′. And by this filtering process, the filtering-processed image signal B ₁ is obtained. The size of the filtered image signal B ₁ is 1/4 of the size of the original image. Then, the filtering process is performed again on the filtered image signal B ₁ by the filter F every other pixel. Then, the filtering process by the filter F is repeatedly performed on the filtered image subjected to the filtering process to obtain n filtered image signals B _k (k = 1).
~ N) are obtained. This filtered image signal B
_k has a size of 1/2 ^2k with respect to the original image.
Here, the frequency response of the filtered image signal B _k is shown in FIG. As shown in FIG. 10, the response of the filtered image signal B _k is such that the higher the k, the higher the high-frequency component is removed (note that k = 1 to 3 in FIG. 10).

Next, the filtering operation image signal B _k thus obtained is subjected to interpolation operation processing in the interpolation operation processing means 11 to obtain a multi-resolution image having the same size as the original image. . Hereinafter, the interpolation calculation processing will be described.

As the interpolation calculation method, various methods such as a method using a B-spline can be cited. In the embodiment of the present invention, however, since the filter F based on the Gaussian signal is used as the low-pass filter, the interpolation calculation is performed. It is assumed that a Gaussian signal is used as the interpolation coefficient for performing. Here, the interpolation function using the Gaussian signal is the following equation (6)

[0042]

(Equation 3)

In, a value approximated by σ = 2 ^n-1 is used.

When the filtering process image signal B ₁ is interpolated, σ = 1 because n = 0. In Expression (6), the filter for performing interpolation when σ = 1 is a 5 × 1 one-dimensional filter as shown in FIG. First, the filtering-processed image signal B ₁ is enlarged to have the same size as the original image by interpolating the pixels each having a value of 0 at every other pixel with respect to the filtering-processed image signal B ₁ . The filtering-processed image signal B ₁ in which the pixels of which the value is 0 are interpolated is one-dimensionally shown in FIG. Then, the filtering processing image signal B ₁ thus interpolated is filtered by the filter F ₁ shown in FIG.

Here, the filter F ₁ shown in FIG. 11 is 5 × 1.
12, the filtering-processed image signal B ₁ is interpolated every other pixel with a value of 0 as shown in FIG. Therefore, performing the filtering process by the filter F ₁ is substantially the same as the 2 × 1 filter (0.5, 0.5) and the 3 × 1 filter (0.1,
The two types of filters (0.8, 0.1) are equivalent to performing the filtering process on the filtering processed image signal B ₁ . By this filtering process, the same number of data as the original image signal Sorg, that is, the blurred image signal Sus1 'of the same size as the original image is obtained.

Then, the filtered image signal B ₂
Is subjected to a filtering process. When interpolating the filtering-processed image signal B ₂ , since n = 1, σ = 2. In Equation (6), the filter for performing interpolation when σ = 2 is a 11 × 1 one-dimensional filter as shown in FIG. Then, first, the filtering-processed image signal B ₂ is enlarged to the same size as the original image by interpolating three pixels each having a value of 0 every other pixel with respect to the filtering-processed image signal B ₂ .
A filtering-processed image signal B ₂ in which pixels of which the value is 0 is interpolated is shown one-dimensionally in FIG. Then, the filtering processing image signal B ₂ thus interpolated is filtered by the filter F ₂ shown in FIG.

Here, the filter F ₂ shown in FIG.
Although it is a filter of 1, as shown in FIG. 14, the filtered image signal B ₂ is interpolated every other pixel with a value of 0. Therefore, performing the filtering process by the filter F ₂ is substantially the same as the 2 × 1 filter (0.5, 0.5) and the 3 × 1 filter (0.3,
0.65,0.05), (0.13,0.74,0.1
3) and (0.05, 0.65, 0.3) of the four types of filters are equivalent to performing the filtering process on the filtering-processed image signal B ₂ . By this filtering process, a blurred image signal Sus2 'having the same data number as the original image signal Sorg is obtained.

Then, this filtering process is performed on all the filtered image signals B _k . When the filtering processed image signal B _k is interpolated, a filter having a length of 3 × 2 ⁿ −1 is created based on the above equation (6), and a value is set between each pixel of the filtering processed image signal B _k. By interpolating 2 ⁿ −1 pixels each having 0, the image is enlarged to the same size as the original image. 3 × 2 for the filtering processed image signal B _k in which the pixel of which the value is 0 is interpolated
Filtering processing is performed by a filter having a length of ⁿ −1.

Here, performing the filtering process with the filter having the length of 3 × 2 ⁿ −1 is equivalent to performing the filtering process with the filter having the length of 2 or 3 in the 2 ⁿ cycle. . By this filtering process, n blurred image signals Susk 'are obtained. Representing this blurred image signal Susk ′ as a visible image,
As a result, blurred images having different resolutions, that is, different resolutions and different frequency responses are obtained. In this way, although the filter becomes long, it is substantially the same as that the filtering process is performed by the filter having the length of 2 or 3, so that the calculation amount does not increase so much even if the filter becomes long. It is a thing. Therefore, the amount of calculation is reduced, and the blurred image signal Sus of the multi-resolution is generated.
It is possible to create k ′ at high speed.

In the embodiment of the present invention, the one-dimensional filter having a length of 3 × 2 ⁿ -1 is used to perform the filtering process in the x direction and the y direction of the image. It is also possible to create a blurred filter and apply the filtering process to the filtered image to obtain the blurred image signal Susk ′. In this case, the filter for performing the filtering process for performing the interpolation calculation on the filtering process image is (3 × 2 ⁿ −
1) × (3 × 2 ⁿ -1) filters, but 2 ⁿ cycles of 2 are used as in the case of using the one-dimensional filter described above.
This is equivalent to performing a filtering process with a × 2 or 3 × 3 filter, and as in the case of using the one-dimensional filter described above, even if the size of the filter is large, the amount of calculation for performing the filtering process is large. Is practically not that large.

Blurred image signal Sus thus obtained
Figure 15 shows the frequency characteristics of k '. As shown in FIG. 15, the higher the value of k of the blurred image signal Susk ', the more the high frequency component of the original image signal Sorg is removed.

Next, processing is performed in the band-limited image signal creating means 3 and the converting means 4. FIG. 16 is a diagram showing an apparatus for performing the processing performed in the band-limited image signal creating means 3 and the converting means 4. As shown in FIG. 16, a band-limited image signal is created based on the plurality of blurred image signals Susk 'created by the above-described blurred image signal creating means 2. This band-limited image signal is a subtractor
According to 21, it is obtained by subtracting the blurred image signal Susk 'between the frequency bands adjacent to each other. That is, Sorg'-Sus1 ', Sus1'-Sus2', ... Sus
A plurality of band-limited image signals are obtained by sequentially calculating N-1'-SusN '. The frequency characteristic of this band-limited image signal is shown in FIG. As shown in FIG. 17, as the band-limited image signal has a larger value of k of the blurred image signal Susk ′,
It becomes a signal representing the low frequency component band of the original image signal Sorg.

Then, the conversion means 4 converts the band-limited image signal thus obtained according to the signal of the band-limited image signal. This conversion is performed in the converter 22 by the function fu as shown in FIG.

The band-limited image signal converted by such a function fu is input to the arithmetic unit 23 including the adding means 5 and the blur mask processing means 6 described above. In the arithmetic unit 23, the following processing is performed. First, the band-limited image signal converted by the function fu as described above (hereinafter, referred to as a converted band-limited image signal) is added. When the addition value is obtained, the blur mask processing means 6 multiplies the enhancement degree β according to the value of the grid image removal signal S ′, and the multiplication value multiplied by the enhancement degree β is the original image signal Sorg. Image signal S that has been added and processed
proc is obtained.

The processing carried out in the band-limited image signal creating means 3, converting means 4, adding means 5 and blur mask processing means 6 is shown in the following equation (7).

Sproc = Sorg + β (S ′) · Fusm (Sorg ′, Sus1 ′, Sus2 ′, ... SusN ′) Fusm (Sorg ′, Sus1 ′, Sus2 ′, ... SusN ′) = {fu1 (Sorg ′ − Sus1 ') + fu2 (Sus1'-Sus2') + ... + fuN (SusN-1'-SusN ')} (7) where Sproc: processed image signal Sorg: original image signal Susk' (k = 1 to N) ): Blurred image signal fuk (k = 1 to N): Function for converting each band-limited image signal In the above embodiment, the processed image signal Sproc is obtained by the equation (7). The processed image signal Sproc may be obtained by the following equation (8). The difference between Expression (7) and Expression (8) is that when obtaining a band-limited image signal, subtraction is performed between adjacent frequency bands in Expression (7), but in Expression (8),
The difference lies in that subtraction processing is performed on the blurred image signal Susk 'of all frequency bands and the original image signal Sorg.

Sproc = Sorg + β (S ′) · Fusm (S ′, Sus1 ′, Sus2 ′, ... SusN ′) Fusm (S ′, Sus1 ′, Sus2 ′, ... SusN ′) = (1 / N) · {Fu1 (S'-Sus1 ') + fu2 (S'-Sus2') + ... + fuN (S'-SusN)} (8) where Sproc: image signal with high-frequency components emphasized Sorg: original image signal Susk ′ (K = 1 to N): Blurred image signal fuk (k = 1 to N): Function for converting each of the band-limited image signals In the above embodiment, interpolation calculation is performed using a Gaussian signal filter. I am trying to process it, but B
Interpolation calculation processing may be performed on the filtered image by spline interpolation calculation. Hereinafter, the B-spline interpolation calculation processing will be described.

The B-spline interpolation calculation is an interpolation calculation method for obtaining interpolation image data for reproducing a smooth secondary image having a relatively low sharpness. This B-spline interpolation operation does not require passing through the original sample points (pixels), but instead, the first derivative and the second derivative (represented by f ″ (X)) are continuous between the sections. Need to be done.

That is, in f _k (x) = A _k x ³ + B _k x ² + C _k x + D _k (9) (B _k in the equation (9) is a coefficient used for convenience and is different from the filtered image. ), F _k ′ (X _k ) = f _k−1 ′ (X _k ) ... (10) f _k ′ (X _{k + 1} ) = f _{k + 1} ′ (X _{k + 1} ) ... (11) f _{The condition is k} ″ (X _k ) = f _k−1 ″ (X _k ) ... (12) f _k ″ (X _{k + 1} ) = f _{k + 1} ″ (X _{k + 1} ) ... (13) However, X _k-1 first-order differential coefficient at the picture element X _k is with respect to the picture elements X _k and X _{k + 1}
For bets, because that match these image signals _{Y k-1, Y k +} 1 gradient _{(Y k + 1 -Y k-} 1) / (X k + 1 -X k-1) is a condition , It is necessary to satisfy the following formula (14).

F _k ′ (X _k ) = (Y _{k + 1} −Y _k−1 ) / (X _{k + 1} −X _k−1 ) ... (14) Similarly, the first floor of the pixel X _{k + 1} For the differential coefficients X _k and X _{k + 2} which are the pixels before and after the pixel X _{k + 1} ,
The gradient of these image signals Y _k , Y _{k + 2} (Y _{k + 2} −Y _k )
/ (X _{k + 2} −X _k )
It is necessary to satisfy the following formula (15).

F _k ′ (X _{k + 1} ) = (Y _{k + 2-} Y _k ) / (X _{k + 2-} X _k ) ... (15) Further, the function f (X) is generally expressed by the following equation (14). It is approximated by what is shown.

F (X) = f (0) + f ′ (0) X + {f ″ (0) / 2} X ² (16) Here, each section X _{k−2 to} X _k−1 , X _{k -1} ~ X _k , X _k ~
The interval of X _{k + 1} , X _{k + 1 to} X _{k + 2} (called a grid interval) is 1
And then, if the position of the interpolation point X _p in the pixel X _{k + 1} directions from the pixel X _k and t (0 ≦ t ≦ 1) , the equation (10) to (13) and (16), f _{k '(0) = C k =} (Y k + 1 -Y k-1) / 2 f k' (1) = 3A k + 2B k + C k = (Y k + 2 -
_{Y k) / 2 f k "} (0) = Y k + 1 -2Y k + Y k-1 = 2B _{Therefore, A k = (Y k +} 2 -3Y k + 1 + 3Y k -Y k-1) / 6 B _k = (Y _{k + 1} −2Y _k + Y _k−1 ) / 2 C _k = (Y _{k + 1} −Y _k−1 ) / 2 Since D _k is unknown, D _k = (D ₁ Y _{k + 2} + D ₂ Y _{k + 1} + D ₃ Y _k + D ₄ Y
_k-1 ) / 6. Further, as described above, the spline interpolation function f _k (x) performs the variable transformation of X = t, so that f _k (x) = f _k (t). Therefore, f _k (t) = ｛(Y _{k + 2} −3Y _{k + 1} + 3Y _k −Y _k−1 )
/ 6｝ t ³ + ｛(Y _{k + 1} -2Y _k + Y _{k -1} ) / 2｝ t ²
+ ｛(Y _{k + 1} −Y _k−1 ) / 2｝ t + (D ₁ Y _{k + 2} + D ₂
Y _{k + 1} + D ₃ Y _k + D ₄ Y _k−1 ) / 6, which are converted into image signals Y _k−1 , Y _k , Y _{k + 1} , and Y _{k + 2.}
Can be expressed by the following formula (17).

F _k (t) = {(− t ³ + 3t ² −3t + D ₄ ) / 6} Y _k−1 + {(3t ³ −6t ² + D ₃ ) / 6} Y _k + {(− 3t ³ + 3t ² + 3t + D ₂ ) / 6} Y _{k + 1} + {(t ³ + D ₁ ) / 6} Y _{k + 2} (17) Here, if t = 1, then f _k (1) = {(D ₄ -1) / 6} Y _k-1 + {(D ₃ −
3) / 6｝ Y _k + ｛(D ₂ +3) / 6｝ Y _{k + 1} + ｛(D
₁ +1) / 6} Y _{k + 2} Next, the equation (17) for the sections X _{k + 1 to} X _{k + 2} is f _{k + 1} (t) = {(− t ³ + 3t ² −3t + D ₄ ) / _{6} Y k + {(3t} 3 -6t 2 + D 3) / 6} Y k + 1 + {(- 3t 3 + 3t 2 + 3t + D 2) / 6} Y k + 2 + {(t 3 + D 1) / 6 _{} Y k + 3 ... (18} ) here, if put as _{t = 0, f k + 1} (0) = (D 4/6) Y k + (D 3/6) Y k + 1
+ (D _2/6) corresponding to _{Y k + 2 + (D 1} /6) Y k + 3 continuity conditions _{(f k (1) = f} k + 1 (0)), and each of the filtering-processed image signal D ₄ −1 = 0, D ₃ -3 = D ₄ ,
D ₂ + 3 = D ₃ , D ₁ + 1 = D ₂ , D ₁ = 0, and
Therefore, _{D k = (Y k + 1} + 4Y k + Y k-1) / 6. _{Therefore, Y p = f k (t} ) = {(- t 3 + 3t 2 -3t + 1) / 6} Y k-1 + {(3t 3 -6t 2 +4) / 6} Y k + {(- 3t 3 + 3t ^{2 + 3t + 1) / 6} } Y k + 1 + {t 3/6} Y k + 2 ... (19) Thus, the filtering-processed image signal Y _k-1,
Interpolation coefficients b respectively corresponding to Y _k , Y _{k + 1} , and Y _{k + 2
k−1} , b _k , b _{k + 1} , and b _{k + 2} are represented by b _k−1 = (− t ³ + 3t ² −3t + 1) / 6 b _k = (3t ³ −6t ² +4) / 6 b _{k + ^{1 = (- 3t 3 + 3t}} 2 + 3t + 1) / 6 a ^{_{b k + 2 = t 3/}} 6.

The above calculation is performed for each section X _{k-2 to} X _k-1 , X.
_{k-1 ~X k,} by repeating the _{X k ~X k + 1, X} k + 1 ~X k + 2, obtaining the different interpolation image signal spaced from the filtering-processed image signal for the entire filtering-processed image signal be able to.

Therefore, by applying the B-spline interpolation calculation processing to each filtering processed image signal B _k , a blurred image signal Susk ′ corresponding to each filtering processed image signal B _k can be obtained.

Further, in the embodiment according to the above equations (7) and (8), the image signal S'is subjected to the filtering processing every other pixel and further subjected to the interpolation operation to obtain the blurred image signal Susk '. However, the present invention is not limited to this, and by performing a filtering process on the image signal S ′ using blur masks of a plurality of sizes, a plurality of blurred image signals Susk ′ having different frequency response characteristics can be obtained. May be obtained.

[Brief description of the drawings]

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

FIG. 2 is a diagram showing frequency characteristics of a grid removal filter.

FIG. 3 is a diagram illustrating a filter (one-dimensional) used in a blurred image signal creating unit.

FIG. 4 is a diagram showing a filter (two-dimensional) used in a blurred image signal generating means.

FIG. 5 is a diagram showing a function for converting a band-limited image signal in a conversion unit.

FIG. 6 is a diagram showing a difference in signal value with and without a grid.

FIG. 7 is a diagram showing the concept of an apparatus in which an image processing method according to the present invention is applied to Japanese Patent Application No. 7-252088.

FIG. 8 is a diagram for explaining a process performed by a filtering unit according to another embodiment of the present invention.

FIG. 9 is a diagram for explaining details of filtering processing.

FIG. 10 is a diagram showing a frequency response of a filtered image signal.

FIG. 11 is a diagram showing a filter used for interpolation calculation of a filtering-processed image signal B ₁ .

FIG. 12 is a diagram showing details of interpolation calculation.

FIG. 13 is a diagram showing a filter used for interpolation calculation of a filtering processed image signal B ₂ .

FIG. 14 is a diagram one-dimensionally representing a filtering-processed image signal B ₂ in which pixels having a value of 0 are interpolated.

FIG. 15 is a diagram showing frequency characteristics of a blurred image signal.

FIG. 16 is a diagram showing an apparatus for performing processing performed in band-limited image signal creating means and converting means.

FIG. 17 is a diagram showing frequency characteristics of a band-limited image signal.

FIG. 18 is a diagram for explaining masking processing in the vicinity of an edge portion.

FIG. 19 is a diagram showing spatial frequency characteristics in a direction orthogonal to the striped pattern of the grid image.

FIG. 20 is a diagram showing the graph of FIG. 19 and aliasing of image signals sampled at sampling intervals corresponding to 2.5 cycles / mm.

21 is a diagram showing a spatial frequency characteristic of an image carried by an image signal sampled at the same sampling intervals as in FIG.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Grid image removing means 2 Blurred image signal creating means 3 Band-limited image signal creating means 4 Converting means 5 Addition means 6 Blurring mask processing means 10 Filtering processing means 11 Interpolation operation processing means 21 Subtractor 22 Converter 23 Operator

Claims (2)

[Claims] 1. An original image signal representing an original image composed of a striped grid image and a radiation image corresponding to the grid by performing imaging using a grid, and a signal relating to a high frequency component of the original image. In the image processing method of emphasizing the high frequency component of the original image, the grid image removal signal is obtained by performing a process of removing the grid image on the original image signal, and a high frequency component of the original image is added. An image processing method, characterized in that a signal related to the grid image is generated based on the grid image removal signal. 2. An original image signal representing an original image composed of a striped grid image corresponding to the grid and a radiographic image by performing imaging using a grid, and a signal relating to a high frequency component of the original image. In the image processing device that emphasizes the high frequency component of the original image by adding, the original image composed of the striped grid image corresponding to the grid and the radiation image by performing imaging using the grid. An image including high-frequency signal calculating means for calculating a signal related to the high-frequency component of the The processing device further includes grid image removing means for performing a process of removing the grid image on the original image signal to obtain a grid image removing signal. , The high frequency signal calculation means, an image processing apparatus, characterized in that the means for producing a signal relating to frequency components of the original image based on the grid image removing signal.