JPH09161061A - Image processing method and processor therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To apply an optimal emphasis processing according to a photographing menu at the time of obtaining an image among images of plural frequency bands obtained by converting the image into a multiple resolution space. SOLUTION: A multiple resolution break-down processing means 2 breaks down an image signal inputted from an image input means 1 to multiple resolution images by a method such as Laplacian pyramid. A selection means 3 selects frequency band and emphasis degree for emphasis processing according to a photographing menu inputted from a photographing menu inputting means 7. An emphasis processing means 4 applies emphasis processing to the image of the selected frequency band. A restoring processing means 5 restores the emphasis-processed image and the other pictures to obtain a processed image signal S'. An image output means 6 reproduces the processed image signal S' as a visible image.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method and apparatus for performing image processing such as emphasis processing on a predetermined frequency band of an original image.

[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, a method of performing frequency enhancement processing such as blurring mask processing on an image signal has been proposed by the applicant (Japanese Patent Laid-Open No. 55-163772). This frequency processing is a process of adding a product obtained by subtracting the blur mask signal from the image signal representing the original image and multiplying the product by the degree of emphasis, and by this, a predetermined spatial frequency component is emphasized in the image. is there.

As another method for performing frequency processing on an image signal, Fourier transform, wavelet transform,
By converting the image into a multi-resolution image by subband conversion, etc., the image signal representing the image is decomposed into signals in multiple frequency bands, and among these decomposed signals, signals in the desired frequency band are emphasized. There has been proposed a method of performing a predetermined image processing such as.

In recent years, in the field of image processing, a Laplacian pyramid method has been proposed as a new method for converting an image into a multi-resolution space (for example, Japanese Patent Laid-Open Nos. 5-244508 and 6-301766). In this Laplacian pyramid, the original image is masked with a mask similar to a Gaussian function, and then the image is sub-sampled to reduce the number of pixels by half to halve the original image. A blurred image of size is obtained, pixels having a value of 0 are interpolated into the sampled pixels of this blurred image to return to an image of the original size, and this image is further masked by the mask described above. Get a blurred image,
This blurred image is subtracted from the original image to obtain a detailed image representing a predetermined frequency band of the original image. This process is repeated for the blurred image to obtain 1 of the original image.
N pieces of blurred images of size 2 / ^2N are created.
Here, since an image masked by a mask similar to a Gaussian function is sampled, a Gaussian filter is actually used.
A processed image similar to that obtained by applying the Laplacian filter is obtained. Since an image in the low frequency band having a size of 1/2 ^2N is sequentially obtained from the image of the original image size in this manner, the image obtained as a result of this processing is called a Laplacian pyramid.

Regarding this Laplacian pyramid, Burt PJ, "Fast Filter Transforms for Image"
Processing ”, Computer Graphics and Image Proces
Sing16, 20-51, 1981; Crowley JL, Stern R.
M., “Fast Computation of the Difference of Low ・ Pass
Transform ”IEEETrans.on Pattern Analysis and Machi
ne Intelligence, Volume 6, Issue 2, March 1984, Mallat S.
G., “A Theory for Multiresolution Signal Decomposit
ion ； The Wavelet Representation ”IEEETrans.on Pat
tern Analysis and Machine Intelligence, Volume 11, 7
Issue, July 1989; Ebrahimi T., Kunt M., “Image compre
ssion by Gabor Expansion ”, Optical Engineering, 30
Volume 7, Issue 873-880, July 1991, and Pieter V
uylsteke, Emile Schoeters, “Multiscale Image Contr
ast Amplification ”SPIE Vol.2167 Image Processing
(1994), pp551-560 for details.

Then, the image of all the frequency bands of the Laplacian pyramid thus obtained is subjected to the emphasis process, and the image of each frequency band subjected to the emphasis process is inversely transformed to obtain a processed image. The method is the above-mentioned JP-A-5-2445.
08 and JP-A-6-301766. This method uses the following equation y = −m × (−x / m) ^p (x <0) y = m × (−x / m) ^p (x ≧ 0) for the image signal of each frequency band. However, x: pixel value in each pixel of the image y: pixel value in each pixel of the image subjected to the emphasis processing m: range of values that the pixel can take (for example, the range of values that the pixel can take is 10 bits In the case of m = 1023), the image is emphasized. That is,
When the value of p is smaller than 1, the degree of emphasis is high, and when the value of p is larger than 1, the degree of emphasis is decreased and the image is emphasized. And the above-mentioned JP-A-5-244508 and JP-A-6-30
The method described in No. 1766 selects the value of p in the above equation in the range of 0 to 1 and applies the enhancement processing to the pixel values of all the pixels in the image to be enhanced. Is. And the image processed in this way is emphasized in each frequency band,
The image is substantially the same as the image that has been subjected to the blur mask processing by masks of a plurality of sizes in the blur mask processing described above.

[0007]

However, in the methods described in JP-A-5-244508 and JP-A-6-301766, enhancement is applied to images in all frequency bands obtained by multiresolution conversion. Because it has been processed,
Depending on the image, the enhancement process may not be performed properly. For example, in an image obtained by shooting with a reduced radiation dose, the quantum noise of the radiation is conspicuous, so that the image of a relatively high frequency band carrying noise components is similar to other frequency bands. When the emphasis process is applied to the noise, the noise is emphasized, and the noise becomes conspicuous in the resulting image. Further, in the image obtained by performing the energy subtraction process, the artifacts due to the positional deviation at the time of subtraction calculation are conspicuous in the image in the relatively high frequency band. When the enhancement process is performed in the same manner as the frequency band of, the artifact is emphasized, and the resulting image becomes conspicuous. In this way, depending on the shooting menu such as shooting site and shooting conditions,
Since the enhancement process cannot be performed properly, the image obtained by performing the enhancement process is rather difficult to see.

In view of the above circumstances, it is an object of the present invention to provide an image processing method and apparatus capable of obtaining an image suitable for observation regardless of a shooting menu such as a shooting site and shooting conditions.

[0009]

An image processing method and apparatus according to the present invention converts an image into a multi-resolution space, decomposes the image into images of a plurality of frequency bands, and decomposes the plurality of frequencies. An image in a predetermined frequency band of the band is emphasized according to the shooting menu, and the processed image is obtained by inversely transforming the image of the frequency band and the image of another frequency band subjected to the emphasizing process. It is characterized by obtaining.

Here, the shooting menu refers to a shooting site, a shooting condition, or a shooting method when shooting an image.

[0011]

According to the image processing method and apparatus of the present invention, among the images of the plurality of frequency bands converted into the multi-resolution space, the image of the predetermined frequency band is emphasized according to the shooting menu. It is the one. That is, the image in the frequency band to be processed is selected according to the shooting menu and / or the emphasis degree is changed to perform the emphasis process. For this reason, the processing suitable for the shooting menu is performed on the image of each frequency band, and thus it is possible to obtain the processed image suitable for observation according to the shooting menu.

[0012]

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

FIG. 1 is a block diagram showing an outline of an apparatus for carrying out an image processing method according to the present invention. As shown in FIG. 1, an apparatus for carrying out an image processing method according to the present invention comprises an image input means 1 for inputting an image to the apparatus,
A multi-resolution decomposition processing means 2 for performing multi-resolution decomposition processing on an input image, and a frequency band to be emphasized among images of a plurality of frequency bands according to a photographing menu input from the photographing menu input means 7. And a selection means 3 for selecting an emphasis degree, an emphasis processing means 4 for performing an emphasis processing on an image in a selected predetermined frequency band based on the emphasis degree selected by the selection means 3, and an emphasis processing means 4. A restoration processing unit 5 for restoring the image in the frequency band and the image in the other frequency band that have been subjected to the enhancement process to obtain a processed image, and the processed image restored by the restoration processing unit 5 as a visible image. Image output means 6 for reproducing as
It consists of:

Next, the operation of the image processing method according to the present invention will be described. FIG. 2 is a block diagram for explaining the processing performed in the multi-resolution decomposition processing means 2 in FIG. In this embodiment, it is assumed that the image signal S is decomposed into a multi-resolution image by the Laplacian pyramid method. As shown in FIG. 2, when the digital image signal S representing the original image is input to the multi-resolution decomposition processing means 2, the filtering means 10 filters it by a low-pass filter. This low-pass filter substantially corresponds to a two-dimensional Gaussian distribution on a 5 × 5 grid as shown in FIG. 3, for example. This low-pass filter is applied to images of all resolutions as described later.

The image signal S filtered by such a low-pass filter is sampled every other pixel by the filtering means 10 to obtain a low resolution approximate image g ₁ . This low-resolution approximate image g ₁ has a size of ¼ of the original image. Next, the interpolation means 11 interpolates pixels having a value of 0 in the sampled intervals of the low resolution approximate image g ₁ . This interpolation is performed by inserting rows and columns having a value of 0 for each column and each row of the low-resolution approximate image g ₁ . In this way, the low-resolution approximate image g ₁ in which the pixel of which the value is 0 is interpolated is blurred, but the pixel of which the value is 0 is inserted every other pixel, so that the change in the signal value is not smooth. Has become.

After the interpolation is performed in this way,
Further, the low-resolution approximate image g ₁ thus interpolated is filtered again by the low-pass filter shown in FIG. 3 to obtain a low-resolution approximate image g ₁ ′. The low-resolution approximate image g ₁ ′ has a smoother change in signal value than the low-resolution approximate image g _{1 on} which the above-described interpolation is performed. In addition, it is an image in which high frequencies higher than half of the original image are disappeared in terms of frequency band. This is because the size of the image is reduced to 1/4 and pixels having a value of 0 are interpolated every other pixel, and filtering processing is performed by the low-pass filter shown in FIG. This is because the image in the high frequency band is blurred.

Next, the subtractor 12 subtracts the low-resolution approximate image g ₁ ′ from the original image to obtain a detailed image b ₀ . This subtraction is performed between the signals of the pixels corresponding to the original image and the low-resolution approximate image g ₁ ′. Here, since the low-resolution approximate image g ₁ ′ is an image in a frequency band higher than half of the spatial frequency of the original image as described above, the detailed image b ₀ is less than half of the original image. The image shows only the upper frequency band.
That is, as shown in FIG. 4, the detailed image b ₀ represents an image in the frequency band of N / 2 to N of the Nyquist frequency N of the original image.

Next, the low resolution approximate image g ₁ is input to the filtering means 10 and subjected to filtering processing by the low pass filter shown in FIG. Then, the low-resolution approximate image g _{1 that} has been subjected to the filtering process is sampled every other pixel by the filtering means 10, and a low-resolution approximate image g ₂ is obtained. The low-resolution approximate image g ₂ has a size of ¼ of the low-resolution approximate image g ₁ , that is, 1/16 of the original image. Then the interpolation means
At 11, the pixels having a value of 0 are interpolated in the sampled intervals of the low resolution approximate image g ₂ . This interpolation is performed by inserting rows and columns having a value of 0 for each column and each row of the low-resolution approximate image g ₂ . In this way, the value is 0
Although the low-resolution approximate image g ₂ in which the pixels are interpolated is blurred, pixels having a value of 0 are inserted at every other pixel, so that the change in the signal value is not smooth.

After the interpolation is performed in this way,
Further, the low-resolution approximate image g ₂ thus interpolated is filtered again by the low-pass filter shown in FIG. 3 to obtain a low-resolution approximate image g ₂ ′. The low-resolution approximate image g ₂ ′ has a smoother change in signal value than the low-resolution approximate image g _{2 on} which the above-described interpolation is performed. Further, as compared with the low-resolution approximate image g ₁ , the image in the frequency band higher than half is disappeared.

[0020] Then, in the subtracter 12, subtracting from the low-resolution approximate image g ₁ low-resolution approximate image g ₂ 'is carried out, the detail image b ₁ is obtained. This subtraction is a low-resolution approximate image g
_This is performed between signals for pixels corresponding to ₁ and the low-resolution approximate image g ₂ ′. Here, since the low-resolution approximate image g ₂ ′ is a blurred image in a frequency band higher than half of the spatial frequencies of the low-resolution approximate image g ₁ as described above, the detail image b ₁ has a low resolution. Approximate image g
_It is an image showing only the frequency band above half of ₁ . That is, as shown in FIG. 4, the detail image b ₁ is only in the frequency band above half of the low-resolution approximate image g ₁ , that is, N / 4 of the Nyquist frequency N of the original image.
It represents an image in a frequency band of up to N / 2.
As described above, a low-pass filter having a Gaussian distribution is used to perform a filtering process to obtain a detailed image. However, since the filtered image is subtracted from the low-resolution approximate image, the Laplacian filter is substantially used. The result is the same as when the filtering process is performed.

Then, the above-mentioned processing is performed by filtering means.
The low-resolution approximate image g _k (k = 1 to N) filtered and sampled by 10 is sequentially repeated to obtain n detailed images b _k (k = 1) as shown in FIG.
~ N) and the residual image g _L of the low resolution approximate image.
Here, the resolution of the detailed image b _k decreases in order from b ₀ , that is, the frequency band of the image decreases.
Against the Nyquist frequency N of the original image, detail images b _k represents the frequency band of ^{N / 2 k + 1 ~N /} 2 k, the size of the image is a 1/2 ^2k times the original image. That is, the highest resolution detail image b ₀ has the same size as the original image, which is a quarter of the size of the next high resolution detail image b ₁ original image detail image b _0. As described above, since the detail image becomes smaller from the one having the same size as the original image, and the detail image is substantially the same as the one to which the Laplacian filter is applied, the multi-resolution according to the present embodiment is used. The transformation is called the Laplacian Pyramid. Further, the residual image g _L can be regarded as an approximate image having a very low resolution of the original image, and in an extreme case, the residual image g _L is composed of only one image representing the average value of the original image. Becomes The detailed image b _k and the residual image g _L thus obtained are stored in a memory (not shown).

Then, the detailed image b thus obtained
_The enhancement processing means 4 performs enhancement processing on _k . In this emphasis processing, the degree of emphasis and the frequency band of the detail image b _{k to be} emphasized are selected by the selection unit 3 in accordance with the imaging menu input from the imaging menu input unit 7, such as the imaging region, imaging conditions or imaging method, The enhancement processing unit 4 performs enhancement processing on the image in the selected frequency band according to the selected enhancement degree. The selection of the emphasis degree is performed by selecting a table according to the shooting menu from the non-linear tables T0, T1 and T2 having different inclinations as shown in FIG. Non-linear table T
Of the 1, T2 and T3, the one having the highest emphasis degree is the non-linear table T1, and the one having the weakest emphasis is the non-linear table T2.
It is. Here, in FIG. 5, m indicates a range of values that a pixel can take. For example, when the range of values that a pixel can take is 10 bits, m = 1023. Hereinafter, the emphasis degree and frequency band selection and emphasis processing according to the shooting menu will be described as an example.

Since the quantum noise of radiation is conspicuous in an image obtained by photographing with a reduced radiation dose, an image in a relatively high frequency band carrying a noise component is similar to other frequency bands. When the emphasis process is applied to the noise, the noise is emphasized, and the noise becomes conspicuous in the resulting image. Therefore, it is possible to obtain an image with reduced noise by not performing the emphasis process on the detailed image b ₀ in the highest frequency band.

In the image obtained by taking the double contrast image of the stomach, it is desirable to emphasize only the folds on the stomach wall, so the detailed image b ₀ , which has a spatial frequency substantially similar to the folds on the stomach wall, An image more suitable for diagnosis in which the folds on the stomach wall are emphasized can be obtained by performing the emphasis process only on b ₁ .

An image obtained by photographing a child has a low contrast because the dose at the time of photographing is small and the subject is relatively thin. Therefore, in order to prevent the detail images b ₀ and b ₁ in the highest frequency band from being emphasized and to increase the contrast in the detail images b _{2 to} b _{n−1 in} the low frequency band. By performing the enhancement process using a table having a high enhancement degree (for example, the non-linear table T1 in FIG. 5), an image with reduced noise and high contrast can be obtained.

The image obtained by photographing the mammo preferably emphasizes the calcification image and the tumor in the mammo, but the calcification image appears in a relatively high frequency band and the tumor appears in a relatively low frequency band. It is a thing. Therefore, detailed images b ₁ and b _{2 in a} frequency band different from the calcification image and the tumor
The image suitable for diagnosis in which the calcification image and the tumor are emphasized by not emphasizing the calcification image and emphasizing only the detailed images b ₀ , b ₃ , and b _n-1 . Can be obtained.

In the image obtained by performing the energy subtraction processing, the artifacts due to the positional deviation during the subtraction calculation appear in a relatively high frequency band. For this reason, if the image in the relatively high frequency band is subjected to the enhancement processing in the same manner as in the other frequency bands, the artifact is emphasized, and the resulting image becomes conspicuous. Therefore, the detailed image b of the highest frequency band
By not performing the enhancement process on ₀ , an image with no noticeable artifact can be obtained.

Then, the detail image b _{k in} the frequency band and the detail image in the other frequency band which have been subjected to the emphasis process are inversely transformed. The inverse conversion process is performed by the restoration processing means 5 as follows.

FIG. 6 is a diagram showing details of the inverse transformation of the detailed image. First, the residual image g _L is interpolated between the pixels by the interpolating means 14 to obtain an image g _L ′ having a size four times the original size.
It is said. Next, in the adder 15, the interpolated image g
_L 'and perform the addition at the lowest resolution detail image b _n-1 of the corresponding pixels comrades addition image (g _L' get + b _n-1). Then, this added image (g _L ′ + b _n−1 ) is interpolated.
The image data is input to 14, and the interpolation means 14 interpolates between pixels to obtain an image b _n-1 ′ having a size four times the original size.

Next, this image b _n-1 ′ is subjected to the addition of the pixels corresponding to the one-step high resolution image b _n-2 of the detail image b _{n-1 in} the adder 15, and the added addition is performed. The signal (b _n-1 ′ + b _n-2 ) is interpolated by the interpolator 14 at each pixel interval, and the image b having a size four times as large as the detail image b _n-2 is obtained.
_n-2 .

The above processing is repeated and the same processing is applied to the emphasized image b _kp . That is, addition of the emphasized image b _kp and the one-step low resolution image b _k-1 ′ subjected to the above-described processing is performed in the adder 15, and the addition signal (b
_kp + b _k-1 ′) is interpolated between the pixels in the interpolating means 14 to obtain an interpolated signal b _kp ′. This processing is sequentially performed on the higher frequency detail image, and finally, in the adder 15, the interpolation image b ₁ ′ and the detail image b of the highest resolution are obtained.
Addition with ₀ is performed to obtain the processed image signal S '.

The processed image signal S'obtained in this way is input to the image output means 6 and displayed as a visible image. The image output means 6 may be a display means such as a CRT, a recording device for performing optical scanning recording on a photosensitive film, or for that purpose, the image signal is temporarily stored in an image file such as an optical disk or a magnetic disk. It may be a device.

Since the frequency band and the degree of emphasis are selected according to the shooting menu and the emphasis processing is applied to the detailed image converted to the multi-resolution by the Laplacian pyramid in this way, it is suitable for the shooting menu. This processing is performed on the image in each frequency band, which makes it possible to obtain a processed image suitable for observation according to the shooting menu.

In the above-described embodiment, the Laplacian pyramid method is used to convert an image into a multi-resolution image, but the method is not limited to this. For example, wavelet transform or subband transform is used. Other methods such as the above may be used for conversion into a multi-resolution image.

The wavelet transform has been recently developed as a frequency analysis method and is applied to stereo pattern matching, data compression, etc. (OLIVIER RIOUL and MARTIN VETTERLI; Wa).
velets and Signal Processing, IEEE SP MAGAZINE, P.14
-38, OCTOBER 1991, Stephane Mallat; Zero-Crossingsof
a Wavelet Transform, IEEE TRANSACTIONS ON INFORMAT
ION THEORY, VOL.37, NO.4, P.1019-1033, JULY 1991).

This wavelet transform is

[0037]

[Equation 1]

In the equation, the signal is converted into a frequency signal for each of a plurality of frequency bands. That is, if the filtering process is performed by changing the period and reduction ratio of the function h and moving the original signal, it is possible to create a frequency signal adapted to a desired frequency from a fine frequency to a coarse frequency.

On the other hand, the sub-band transform not only obtains images of two frequency bands by one kind of filter like wavelet transform, but also obtains images of plural frequency bands at once by using plural kinds of filters. It is a conversion method that also includes.

Then, as in the case of the Laplacian pyramid described above, the enhancement processing according to the shooting menu is performed on the image for each of the plurality of frequency bands obtained by the wavelet transform or the sub-band transform as described above. Since only the image in the desired frequency band is emphasized, a good image more suitable for observation and interpretation can be obtained.

[Brief description of the drawings]

1 is a block diagram of an apparatus for implementing an image processing method according to the present invention.

FIG. 2 is a diagram showing details of multi-resolution decomposition processing means.

FIG. 3 is a diagram showing a low-pass filter.

FIG. 4 is a diagram showing a detailed image for each of a plurality of frequency bands on which a Laplacian pyramid is applied.

FIG. 5 is a table showing the degree of emphasis

FIG. 6 is a diagram showing details of restoration processing means.

[Explanation of symbols]

 1 image input means 2 multi-resolution decomposition processing means 3 selection means 4 enhancement processing means 5 restoration processing means 6 image output means 7 shooting menu input means 10 filtering means 11 interpolation means 12 subtractor 14 interpolation means 15 adder

Claims (2)

[Claims] 1. An image is converted into a multi-resolution space to decompose the image into images for each of a plurality of frequency bands, and a predetermined one of the plurality of frequency bands is selected according to a shooting menu when the image is obtained. Image processing method, wherein a processed image is obtained by performing an emphasis process on an image in the frequency band of the above and performing inverse transformation on the image of the frequency band subjected to the emphasis process and the image of another frequency band. . 2. Decomposing means for decomposing the image into images for each of a plurality of frequency bands by converting the image into a multi-resolution space, and the plurality of frequency bands according to a photographing menu when the image is obtained. Among these, an emphasis processing means for performing an emphasis process on an image in a predetermined frequency band, and an inverse process for inversely converting the image in the frequency band subjected to the emphasis process and the image in another frequency band to obtain a processed image An image processing apparatus comprising: a conversion unit.