DE69632651T2 - Method and device for image processing - Google Patents

Description

BACKGROUND THE INVENTION

Field of the invention

The The invention relates to the processing for forming a plurality of blur image signals with different frequency characteristics from an image signal.

description of the prior art

methods for obtaining an image signal representing an image, to perform adequate image processing with the image signal and subsequent reproduction a visible image from the processed image signal so far used in various fields. For example in methods for performing of such methods an image by means of Fourier transform, a wavelet transform, a Subband transformation or the like in multi-resolution images transformed, and the image signal representing image thereby broken down into signals that fall into several different frequency bands. From the decomposed signals is then a in a desired Frequency band falling signal of a predetermined image processing subjected.

When Such a method has been proposed in a method an original image is transformed into multi-resolution images, including a blur mask filter as used in the method using wavelet transform, whereupon a blur image signal representing a blurred image of each solution an image processing performed becomes. The proposed method is used for image processing of Radiation images or the like.

The Applicant has also proposed a method of compressing a dynamic range of an image using a blur image signal and reducing the contrast level of portions of the low-density or high-density image or the contrast levels of the entire image such that the difference between the highest and the lowest lowest image density in the original image may be small, that is, to make the dynamic range of the original image narrow. The method of compressing a dynamic range of an image is disclosed, for example, in U.S. Patent 5,454,044. The proposed method comprises the steps of calculating a blur image signal Sus from the original image signal representing the original image and processing the original image signal according to the formula (3). Sproc = Sorg + f (Sus) (3) where f (Sus) is a function whose value decreases monotonously as the value of the blur image signal Sus increases.

The blur image signal Sus can be obtained by performing the operation according to the following formula (2): Sus = ΣSorg / (M × N) (2)

And although regarding Image signal components of the original image signal Sorg, which represents M × N picture elements, which surround a middle picture element. For example, each one will the picture elements, which are in every second row and every second Column in the box of picture elements representing the picture as taken the middle picture element.

The for the Blurred standing blur image signal Sus, resulting from the blur masks processing results and a lower resolution has as the original image, can also by using a Unsharp mask filter predetermined size and through Calculate the mean or weighted average of the values the one in the blur mask filter located pixels.

In this way, a processed image signal Sproc is obtained which represents an image having a dynamic range narrower than the dynamic range of the original image. With the aid of the proposed method, both the dynamic range of image parts having low image density values and the dynamic range of image parts having high image density values can be suitably compressed. In such cases, in which the Differential quotient of the function f (Sus) is continuous, there is no artificial contour in the image represented by the processed image signal Sproc. In this way, the image density range of the image can be compressed so that the image portions covering a large area of the image density can be used and thereby have good image quality in the reproduced visible image, while the image quality of fine image structures in each of the parts can be maintained at a good value with different image density values.

at the above explained Image processing, thus the original image with the help of blur mask filters in multi-resolution images be transformed, several filters of different sizes used become. In particular, a blur image of low resolution too it is necessary to use a filter of considerable size. If, however, the filter size considerably will, the number of for to perform the filtering process Operations very big, and it takes a long time, to carry out the operations the blur mask processing make. About that in addition, there is a need to store large capacity for storage the information available to represent the multiple filters to the multiple blur images with different resolution values to obtain. That's why the device for performing do not build the processing small, the cost of the device can not kept low.

In such cases, in which the blur image signal through the blur mask filter is formed in the zone near the edge of the picture where the Image density changes abruptly, the blur image signal Deteriorated by the image density at the edge of the image. If Therefore, the above-described dynamic range compression process under Use of the blur image signal carried out will cause problems such as an artifact, overshoot, Under-swing or become an artificial one Contour, therefore, the image quality of the processing picture is not good.

EPIPHANY THE INVENTION

One The aim of the invention is to provide a dynamic range compression method, in which a dynamic range compression process with respect to an original image signal using multiple blur image signals carried out will, so that in a zone nearby of an image edge no artifact arises.

• i) aus dem Originalbildsignal werden mehrere Unschärfemasken-Bildsignale erzeugt, die verschiedene Frequenzgänge besitzen,
• ii) aus dem Originalbildsignal und den mehreren Unschärfemasken-Bildsignalen oder aus den mehreren Unschärfemasken-Bildsignalen werden mehrere bandbegrenzte Bildsignale gebildet, von denen jedes eines der Signale repräsentiert, die in mehrere verschiedene Frequenzbänder des Originalbildsignals fallen,
• iii) Durchführen eines Umwandlungsprozesses bezüglich mindestens eines einzigen bandbegrenzten Bildsignals unter den mehreren bandbegrenzten Bildsignalen in der Wei se, daß zumindest ein Teil des bandbegrenzten Bildsignals klein werden kann, wodurch eine Mehrzahl umgewandelter Bildsignale entsteht,
• iv) Integrieren der mehreren umgewandelten Bildsignale, um ein Integrationssignal zu erzeugen,
• v) Subtrahieren des Integrationssignals von dem Originalbildsignal, um dadurch ein Differenzsignal zu erhalten, und
• vi) Umwandeln des Differenzsignals, um dadurch das Signal bezüglich der niederfrequenten Komponenten des Originalbilds zu gewinnen, welches Signal auf das Originalbildsignal zu addieren ist.

The invention provides a dynamic range compression method in which a signal for low-frequency components of an original image is added to an original image signal representative of the original image to thereby compress the dynamic range of the original image, the method comprising the steps of:

• i) from the original image signal a plurality of unsharp mask image signals are generated, which have different frequency responses,
• ii) from the original image signal and the plurality of unsharp mask image signals or from the plurality of unsharp mask image signals, a plurality of bandlimited image signals each representing one of the signals falling into a plurality of different frequency bands of the original image signal are formed;
• iii) performing a conversion process on at least a single bandlimited image signal among the plurality of bandlimited image signals, in the sense that at least a portion of the bandlimited image signal may become small, thereby producing a plurality of converted image signals,
• iv) integrating the plurality of converted image signals to produce an integration signal,
• v) subtracting the integration signal from the original image signal to thereby obtain a difference signal, and
• vi) converting the difference signal to thereby obtain the signal relating to the low-frequency components of the original image, which signal is to be added to the original image signal.

• i) eine Unschärfemasken-Bildsignal-Erzeugungseinrichtung zum Bilden mehrerer Unschärfemasken-Bildsignale mit unterschiedlichen Frequenzgängen aus dem Originalbildsignal,
• ii) eine Bandbeschränktes-Bildsignal-Erzeugungseinrichtung zum Bilden mehrerer bandbegrenzter Bildsignale, von denen jedes eines der Signale repräsentiert, die in mehrere unterschiedliche Frequenzbänder des Originalbildsignals fallen, aus dem Originalbildsignal und den mehreren Unschärfemasken-Bildsignalen, oder aus den mehreren Unschärfemasken-Bildsignalen,
• iii) eine Umwandlungseinrichtung zum Durchführen einer Umwandlung zumindest eines einzigen bandbegrenzten Bildsignals unter den mehreren bandbegrenzten Bildsigna len in der Weise, daß zumindest ein Teil des bandbegrenzten Bildsignals klein werden kann, um dadurch mehrere umgewandelte Bildsignale zu erhalten,
• iv) eine Integrationseinrichtung zum Integrieren der mehreren umgewandelten Bildsignale, um dadurch ein Integrationssignal zu gewinnen,
• v) eine Subtrahiereinrichtung zum Subtrahieren des Integrationssignals von dem Originalbildsignal, um dadurch ein Differenzsignal zu erhalten, und
• vi) eine Kompressionseinrichtung zum Umwandeln des Differenzsignals, um dadurch das Signal bezüglich der niederfrequenten Komponenten des Originalbilds zu erhalten, welches auf das Originalbildsignal zu addieren ist.

The invention also provides a dynamic range compression device in which a signal relating to low-frequency components of an original image is added to an original image signal for the original image and the dynamic range of the original image is compressed thereby, comprising:

• i) unsharp mask image signal generation means for forming a plurality of unsharp mask image signals having different frequency responses from the original image signal,
• ii) a bandlimited image signal generating means for forming a plurality of bandlimited image signals each of which represents one of the signals falling into a plurality of different frequency bands of the original image signal from the original image signal and the plurality of unsharp image signals, or from the plurality of unsharp mask image signals;
• iii) conversion means for performing a conversion of at least a single bandbe bordered image signal among the plurality of band-limited Bildsigna len in such a way that at least a portion of the band-limited image signal can be small, thereby to obtain a plurality of converted image signals,
• iv) integration means for integrating the plurality of converted image signals to thereby obtain an integration signal,
• v) subtracting means for subtracting the integration signal from the original image signal to thereby obtain a difference signal, and
• vi) compression means for converting the difference signal to thereby obtain the signal relating to the low-frequency components of the original image to be added to the original image signal.

at the dynamic range compression method and the dynamic range compression device according to the invention The conversion process will be at least a single band-limited image signal is performed, which one of a plurality of band-limited image signals, and in such a way that at least a portion or part of the band-limited image signal becomes small. As a result, a plurality of converted image signals are obtained. their Absolute values are subsequently integrated to gain the integration signal. After that it will be Integration signal subtracted from the original image signal. To this Way you have the signal regarding the low-frequency components of the image signal, which on the Original image signal to add is. Therefore, a band limited Image signal with low signal value whose absolute value comparatively is great into a signal that has little effect on the signal with respect to the low-frequency components, which points to the original image signal is to be added, and which is essentially the same characteristic has like a signal, which one with a blur mask small size receives. consequently let yourself in the area of the image edge, where the image density changes abruptly, the Influence of a Weakened signals which leads to an artifact. As a result, you can enter Picture without artifact with good picture quality due to the dynamic range compression method win.

SHORT DESCRIPTION THE DRAWINGS

1 Fig. 10 is a block diagram showing the concept behind the multi-resolution transformation device according to the present invention;

2 FIG. 4 is a block diagram showing how multiple blur image signals are formed; FIG.

3 Fig. 4 is an illustrative view of a (one-dimensional) filter used in the method of forming the blur image signal;

4 is an exemplary representation of how a filtering process is performed

5 is a graphical representation of the frequency characteristic of a filter processing (hereinafter: filtered) image signals illustrated,

6 Fig. 3 is an illustrative view of a (two-dimensional) filter that may be used in a filter processing device,

7 is an illustrative representation of a filter used in an interpolation performed on a filtered image signal B1,

8th is an illustrative representation of how an interpolation is performed

9 is an illustrative representation of a filter used in interpolation of a filter-processed image signal B2,

10 is an illustrative representation of how an interpolation is performed

11 is a graphical representation of the frequency characteristic of a blur image signal,

12 is a block diagram of the concept behind the image processing apparatus according to the invention,

13 Fig. 10 is a block diagram of an embodiment of the image processing apparatus according to the Er contraption,

14 FIG. 4 is a graphical representation of examples of frequency characteristics of bandlimited image signals; FIG.

15 Fig. 10 is a diagram showing an example of a function used in a conversion process performed in a band-limited image signal converting means;

16 Fig. 10 is a graph showing an example of the frequency characteristic of a processed image signal obtained by the image processing apparatus of the present invention;

17 Fig. 15 is a diagram of another example of a function used in a conversion process performed on a band-limited image signal in the conversion means;

18 Fig. 12 is a diagram showing various functional examples used in a conversion process in the conversion apparatus;

19A . 19B . 19C and 19D are illustrative representations showing the problems encountered when all the band-limited image signals are converted with a single mode of operation,

20 Fig. 10 is a block diagram of another embodiment of the image processing apparatus according to the invention;

21 Fig. 10 is a diagram showing an example of a function used in a magnification ratio signal generating means;

22A . 22B . 22C . 22D and 22E are illustrative representations of effects of in 20 shown embodiment,

23A-1 . 23B-1 . 23C-1 . 23D-1 . 23E-1 and 23A-2 . 23B-2 . 23C-2 . 23D-2 . 23E-2 are descriptive representations of the effects of in 20 shown embodiment,

24 FIG. 12 is a block diagram of another another embodiment of the image processing apparatus of the present invention; FIG.

25 FIG. 15 is a diagram showing an example of a function for generating an auxiliary image signal by converting a band-limited signal; FIG.

26 Fig. 10 is a diagram showing another example of a function for generating an auxiliary image signal by converting a band-limited image signal;

27A . 27B . 27C . 27D . 27E and 27F are vivid illustrations of the effects of in 24 shown embodiment,

28 Fig. 4 is a block diagram showing the concept behind the dynamic range conversion device according to the present invention;

29 Fig. 12 is a graph showing an example of a monotone decreasing function for converting a difference signal;

30A and 30B FIG. 16 are graphs of different examples of monotonically decreasing functions for converting a difference signal; FIG.

31 Fig. 12 is a graph showing another other example of a monotone decreasing function for converting a difference signal;

32A and 32B For example, graphs of other examples are monotonically decreasing Functions for converting a difference signal,

33 FIG. 4 is a graphical representation of examples of the frequency characteristic of integration signals; FIG.

34 is a graphical representation of examples of the frequency characteristic of differential signals,

35 is a graphical representation of various examples of frequency characteristics of difference signals,

36 Fig. 12 is a graphic representation of examples of functions used in a conversion process in a conversion device, and Figs

37 Figure 4 is a descriptive illustration of how a blur image signal is formed in a zone near the edge of the image.

DESCRIPTION THE PREFERRED EMBODIMENTS

in the In the following, the invention will be described in greater detail with reference to FIG to the accompanying drawings.

1 Fig. 10 is a block diagram illustrating the concept behind the multi-resolution transforming apparatus of the present invention. As in 1 is shown, the multi-resolution transforming device includes a filtering process device 10 and an interpolation process device 11 , The filter process device 10 performs a filtering process on a received original image signal Sorg. The filtering process (hereinafter referred to as filtering) is performed by using a filter having a predetermined size, and is executed with respect to each of the picture elements selected at predetermined intervals to thereby obtain a first filtered image. Subsequently, the filter process device leads 10 using the predetermined size filter, filters out the first filtered image to thereby obtain a second filtered image. In this way, the filter process device performs 10 successively filtering on individual filtered images through, which are obtained in this way successively. The interpolation process device 11 performs an interpolation operation process (hereinafter simply referred to as interpolation) with respect to the plurality of filtered images to thereby obtain multi-resolution blur image signals, each of which is formed by the same number of pixels as the original image, that is, each has the same size as the original image.

wobei σ = 1. Das Gauss-Signal besitzt eine gute Ortscharakteristik sowohl im Frequenz- als auch im Raumbereich und eignet sich daher als Filter F. 2 FIG. 12 is a block diagram showing how the plurality of unsharp image signals in the in 1 shown multi-resolution transformation device can be obtained. How out 2 As is apparent, the original digital image signal Sorg, which represents the original image, is subjected to filtering, resulting in a low-pass filter L within the filter process device 10 happens. As a low-pass filter L can be used, for example, a 5 × 1 grating filter F, as in 3 is shown. The filter F corresponds approximately to a one-dimensional Gaussian distribution. The filter F can be represented by the formula

where σ = 1. The Gaussian signal has a good local characteristic both in frequency and in space and is therefore suitable as filter F.

The Filtering is done using the filter F along the X and the Y direction in the field of picture elements of the original picture. On this way, the filtering process becomes relative to the entire original image signal Carried out.

In the filter process device 10 the filtering is done by means of the filter F in the manner described below. 4 is an illustrative view showing how the filtering is done. How out 4 shows, the filter with the help of the filter F to 3 for image signal components of the original image signal Sorg, which represents the picture elements lying in every second row and every other column within the field of picture elements of the original picture. From the filter process you get a filtered tes image signal B1. The filtered image signal B1 represents a filtered image having a size which is 1/4 times (1/2 times in both the X and Y directions) the size of the original image. Subsequently, the filtering of the filtered signal B1 is performed using the filter F. The filtering refers to the image signal components of the filtered image signal B1, which represents those pixels which are in every second row and every other column of the array of pixels of the filtered image. Filtering with the aid of the filter F is therefore carried out successively, where n is the number of filtered image signals Bk, with k = 1 to n. Each of the filtered image signals Bk stands for a filtered image having a size which corresponds to the 1/2 ^2k- 1. Times the original image size. In addition, the filtered image signals Bk have the in 5 illustrated frequency response. As in 5 is shown, the frequency characteristic of the filtered image signals Bk is such that the high-frequency components could be successively eliminated with increasing value k. In 5 only the response characteristics of the three filtered image signals Bk for k = 1, 2 and 3 are shown.

In the embodiment described above, the filtering is done by using the one-dimensional filter F after 3 along the X direction and the Y direction of the original image. Alternatively, according to 6 the filtering of the original image signal Sorg can be performed by using a 5 × 5 filter of two-dimensional shape for each of the filtered image signals Bk.

Subsequently, in the interpolation process device 11 to 2 the interpolation of each of the filtered image signals Bk. The interpolation results in blurred images (blur images) having a multiple resolution which are the same size as the original image. The following is an explanation of how the interpolation is performed.

wobei σ = 2^k–1.As far as the interpolation method is concerned, one can use one of several methods, for example a B-spline method. In this embodiment, the filter F corresponding to the Gaussian signal is used as a low-pass filter, and therefore, a Gaussian signal serves as an interpolation coefficient for performing the interpolation. The interpolation coefficient using the Gaussian signal corresponds to the following formula (5):

where σ = 2 ^k-1 .

In the case of interpolation for filtering the image signal B1, k = 1 and therefore σ = 1. In cases where σ = 1 in the above formula (5), the filter used for interpolation becomes identical to a one-dimensional 5 × 1 Filter F1 as it is in 7 is shown. First, a single pixel of value 0 is inserted between two adjacent pixels of the filtered image represented by the filtered image signal B1. In this way, the size of the filtered image corresponding to the filtered image signal B1 is raised to the same size as the original image. 8th illustrates the filtered image signal B1, which was obtained by inserting the pixels with the value 0, shown here as a one-dimensional pattern. Subsequently, the filtering of the filtered image signal B1, which was obtained by inserting the picture elements with the value 0, for which the in 7 represented filter F1 is used.

The filter F1 after 7 is a 5x1 filter. How out 8th As is apparent from the filtering of the image signal B1 obtained by the insertion process, a single pixel of 0 is inserted between every two adjacent pixels. The filtering performed with the aid of the filter F1 with respect to the filtered image signal B1 therefore essentially corresponds to the filtering process carried out with two types of filters, namely a 2 × 1 filter (0.5, 0.5) and a 3 × 1 Filter (0.1, 0.8, 0.1). The filtering results in a blurred or blur image signal Sus1 consisting of the same number of image signal components as the original image signal Sorg, which is an image signal representing a blurred image of the same size as the original image.

Subsequently, a filtering of the filtered image signal B2 takes place. In the interpolation of the filtered image signal B2, k = 2, and hence σ = 2. If σ = 2 in the above formula (5), the filter for performing the interpolation becomes identical to a one-dimensional 11 × 1 filter F2 included in FIG 9 is shown. First, according to 10 three picture elements having a value 0 are inserted between two adjacent picture elements in the filtered picture represented by the filtered picture signal B2. In this way, the size of the filtered image corresponding to the filtered image signal B2 is made the same size as that of the original picture has. Then the filtering is done with the help of the in 9 shown filter F2 with respect to the filtered image signal B2, which was obtained by inserting the pixels with the value 0.

This in 9 shown filter F2 is the 11 × 1 filter. As in 10 In the filtering of the filtered image signal B2 obtained by the insertion, three picture elements having a value of 0 are inserted between every two adjacent picture elements. Therefore, the filtering performed on the filtered image signal B2 by means of the filter F2 becomes substantially equivalent to the filtering performed with four types of filters, namely a 2 × 1 filter (0.5; 0). 5) and 3x1 filters (0.3, 0.65, 0.05), (0.13, 0.74, 0.13) and (0.05, 0.65, 0.3). By this filtering, a blur image signal Sus2 is obtained, which consists of the same number of image signal components as the original image signal Sorg.

The filtering process described above is performed with respect to all the filtered image signals Bk. In the interpolation of the filtered image signals Bk, a filter with the length 3 × 2 ^k -1 is created according to the formula (5). Further, 2 ^k -1 picture elements of 0 are inserted between every two adjacent picture elements of each filtered picture signal Bk, thereby bringing the size of the filtered picture represented by the filtered picture signal Bk to that of the original picture. Subsequently, the filtering of the filtered image signal Bk, which was obtained by inserting the picture elements with the value 0, using the filter with a length of 3 × 2 ^k -1 is performed.

The filtering operation performed on the filter of length 3 × 2 ^k -1 becomes equivalent to the filtering using a filter of length 2 or 3 and having a period of 2 ^k . By filtering, one obtains n blur image signals Susk. When visible images are reproduced from the unsharp image signals Susk, multi-resolution blur images having different resolution levels, that is, different frequency characteristics, are obtained. From the above description, it is apparent that even if the length of the filter becomes large, the filtering process becomes substantially equivalent to performing the filtering process with a filter of length 2 or 3. Therefore, even if the length of the filter becomes large, the filtering process becomes large Number of operations not very big. Thus, one can keep the number of operations small and still quickly obtain multi-resolution blur image signals Susk.

In this embodiment, the filtering along the X direction and the Y direction of the image is performed by means of a one-dimensional filter of length 3 × 2 ^k -1. Alternatively, the filtering of each of the filtered images may be performed by using the two-dimensional filter which has been prepared in advance, and thereby the blur image signals Susk can be obtained. In these cases, as a filter for performing the interpolation operation of the filtered image, a (3 × 2 ^k -1) × (3 × 2 ^k -1) filter is used. As in the cases of the one-dimensional filter described above, the filtering with the two-dimensional filter becomes equivalent to a filtering operation performed by using a 2 × 2 or a 3 × 3 filter having a period of 2 ^k . Thus, in the cases where the one-dimensional filter described above is used, even if the size of the filter becomes large, the amount of operations to be performed for the filtering operation does not become very large.

As outlined above was, can in the individual steps, images of low resolution by the multi-resolution transformation method and the accompanying Device according to the invention also be won if the size of the used for filtering Filters is not high. That's why you can avoid problems which occur because of it a big Amount of to be performed Operations are due to a size increase of the filter. in the Result can be quickly win blur image signals. In addition, will the filtering was carried out repeatedly with the same filter. hereby Needless itself the use of multiple filters of different sizes. Accordingly it is also superfluous, one Memory big capacity to store the information for to provide the many filters, one can use the multi-resolution transforming device form with a small size.

The blur image signals Susk obtained in the above-described manner have the in 11 shown frequency characteristic. As in 11 1, the blur image signals Susk have such a frequency characteristic that high-frequency components of the original image signal Sorg have been successively eliminated with increasing value of k.

20 Fig. 10 is a block diagram of this embodiment of the image processing apparatus according to the present invention. As in the above-described embodiment, band-limited image signals are formed from the original image signal Sorg, and a plurality of blur image signals Susk obtained by the blur image signal generator 1 were generated. Each of the bandlimited image signals is from the subtractor 21 obtained the blur image signals Susk two adjacent frequency bands (for example Original image signal Sorg, the signals Sorg and Sus1) are subtracted from each other. Specifically, the values of Sorg-Sus1, Sus1-Sus2, ..., SusN-1-SusN are successively calculated to obtain the plurality of band-limited image signals. In this embodiment, for example, the signal Sus2-Sus3 is used as the corresponding auxiliary signal for the band-limited image signal Sus1-Sus2. Therefore, a single device serves essentially as a band-limited image signal generating means and as an auxiliary image signal generating means. More specifically, the signal formed by the band-limited image signal generating means is processed as a band-limited image signal and is further processed as the subsidiary image signal corresponding to the adjacent band-limited image signal.

at this embodiment Various modifications are possible, for example with regard to the functions f and g.

in the Following are exemplary embodiments dynamic range compression method and dynamic range compression device according to the invention explains in which the dynamic range of the original image signal with the aid of Unsharp image signals is compressed.

28 is a block diagram of one embodiment of the dynamic range compression device according to the invention, in which the in 1 shown multi-resolution transformation device is used as a blur image signal generating device. As in 28 As can be seen, the inventive dynamic range compression device includes a blur image signal generator 1 for forming a multi-resolution blur image signal Susk with k = 1 to n, which has different frequency characteristics, from a received original image signal Sorg. The dynamic range compression device also includes a band limited image signal generating device 2 for generating a plurality of band-limited image signals from the blur image signals Susk received from the blur image signal generation means 1 were formed. The dynamic range compression device further comprises conversion means for performing a conversion process on at least a single band-limited image signal among the plurality of band-limited image signals received from the band-limited image signal generation means 2 were generated so that at least a part of the band-limited image signal becomes small. The dynamic range compression device further includes an integrator 4 for integrating the converted bandlimited image signals received from the converting means 3 were obtained to thereby generate an integration signal. The dynamic range compression device also includes a subtractor 6 for subtracting the integration signal from the original image signal Sorg to thereby obtain a difference signal. The dynamic range compression device further includes a compression processing device 7 for converting the difference signal, for adding the converted difference signal to the original image signal Sorg, to thereby obtain a processed image signal Sproc in which the dynamic range of the original image has been compressed.

Basically, the structure of the embodiment of the dynamic range compression device as in 13 representing the image processing apparatus for performing the frequency emphasis processing described above. However, different are in the surgical facility 23 built-in devices other than those used for frequency emphasis processing. The embodiment of the dynamic range compression device will be described below with reference to FIG 13 explained.

As in 13 is shown, the plurality of blur image signals Susk received from the blur image signal generator 1 subsequently formed in the band-limited image signal generating means 2 and in the converter 3 processed. First, the band-limited image signals are generated from the original image signal Sorg and the plurality of unsharp image signals Susk obtained from the unsharp image signal generating means 1 were formed. Each of the bandlimited image signals is from the subtractor 21 obtained subtracting the blur image signals Susk two adjacent frequency bands (the original image signal sorg is Sorg and Sus1) from each other. In particular, the values Sorg-Sus1, Sus1-Sus2, ... SusN-1-SusN are successively calculated to obtain a plurality of band-limited image signals.

14 shows the frequency response of the band-limited image signals. As in 14 1, the band-limited image signals have a frequency characteristic according to which the bands of the low-frequency components of the original image signal Sorg are displayed as the value of k of the unsharp image signal Susk becomes large.

Subsequently, in the conversion device 3 each of the band-limited image signals is converted according to its level. The conversion of each band-limited image signal is performed by the converter 22 using, for example, the in 15 represented function f. As with the frequency emphasis processing, the same function can be used for the individual band-limited image signals, or other functions can be used for the band-limited image signals.

The band-limited image signals converted with the function f are transferred to the operation device 23 entered with the integration device 4 , the subtractor 6 and the compression process device 7 equipped as already explained above. In the operation facility 23 the execution of the processing is performed as described below. First, the band-limited image signals converted by the function f are integrated to obtain an integration signal, which is then subtracted in the subtracter 6 from the original image signal Sorg to obtain a difference signal. Thereafter, the difference signal in the compression process device 7 is converted, and the converted difference signal is added to the original image signal Sorg to obtain the processed image signal Sproc. As the function D for converting the difference signal, for example, a monotone decreasing function as shown in FIG 29 is shown. Alternatively, a function may be used as function D, as in 30A is shown, and has such a course that the value obtained by the function can only change with respect to a zone with a comparatively low signal value. As a further alternative, as function D, the in 30B shown function used who according to the course of which the value obtained by the function can only change in a zone with a comparatively large signal value. As a further alternative, as function D, a in 31 shown monotone decreasing function are used, in which the differential quotient is continuous. As yet another alternative, as function D, the in 32A according to which the value obtained by the function can only change in a zone with a comparatively small signal value, the functional pattern containing no sharp bend. As another alternative, as a function of in 32B according to which the value obtained by the function can only change in a zone with a comparatively low signal value, the functional pattern having no sharp bend.

The above-explained, from the limited image signal generating means 2 , the conversion device 3 , the integration facility 4 , the subtractor 6 and the compression process device 7 The processing carried out can be represented by the following formula (13): Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg - Sus1) + f2 (Sus1 - Sus2) + ... + fk (Susk-1-Susk) + ... + fN (SusN-1-SusN)} (13) where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 1 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc.

The integration signal, which with the help of the integration device 4 has won the in 33 illustrated frequency response. In 33 the solid line means the frequency response of the integration signal corresponding to a flat zone in which there is no edge. In addition, the dashed line indicates the frequency response of the integration signal for a zone near the edge. The frequency response of the difference signal obtained by subtracting the integration signal from the original image signal Sorg is in 34 shown. The above-described band-limited image signals have such a characteristic that in a flat zone where the change of the image density of the original image is comparatively weak, the absolute value of the signal value of each band-limited image signal in each frequency band may be small. How else in 37 is seen in a zone near an image edge where the image density abruptly changes in those cases where the band-limited image signal belongs to a comparatively low-frequency band, that is, in cases where the size of the unsharp mask is included the formation of the blur image signal Susk, which is comparatively large, includes the image edge in the blur mask arranged for a pixel located near the edge. Therefore, in these cases, the band-limited image signal is adversely affected by the edge, and the absolute value of the signal value of the band-limited signal becomes comparatively large. In this way, a portion that does not represent the image edge is adversely affected by the image density value of the edge, and it comes in the edge region of the image obtained by the dynamic range Kgmpressionsvorgang to an artifact, such as an overshoot or undershoot.

The band-limited image signal is therefore with the above function fk converted so that in such cases, in which the absolute value of the band-limited image signal is greater than the threshold Th1 is, the absolute value can become small. The Absolute values of the band-limited image signals obtained in this way are integrated and integrated Signal is subtracted from the original image signal Sorg. To this Way, a signal corresponding to the blur image signal Sus in the above obtained formula (3). In the embodiment of the dynamic range compression device according to the invention The term "blur image signal" should be understood in a narrow sense and that the blur image signal Sus in the above formula (3) corresponding signal is called difference signal designated.

As in 34 is shown in the flat zone in which there is no edge, the difference signal has the frequency response shown by the solid line. In addition, according to the dashed line in FIG 34 the difference signal in the zone near an edge has such a frequency response that it can also contain a comparatively high-frequency band. Thus, the same effects can be achieved as when the size of the unsharp mask used to form the difference signal is smaller than the size of the current unsharp mask in the zone near one edge.

On this way, the band-limited signal becomes the zone near an edge with a small signal value whose absolute value comparatively is great converted into the signal, which has little influence on the Signal regarding the low-frequency components, which points to the original image signal Sorg should be added. Consequently, within the zone becomes close an edge where the image density changes abruptly, the influence of a Weakened signal, which causes the occurrence of an artifact. As a result, a Image obtained without artifact in good image quality through the dynamic range compression process.

In the embodiment described above, the processed signal Sproc is obtained by the formula (13). Alternatively, the processed image signal Sproc may be obtained by the formula (14) given below. The formulas (13) and (14) differ in the point explained below. In particular, according to formula (13), the band-limited image signal is obtained by subtracting the signals of two adjacent frequency bands from each other. On the other hand, according to the formula (14), the band-limited signal is obtained by subtracting each of the unsharp image signals Susk of all the frequency bands from the original image signal Sorg. The difference signal obtained by the formula (14) has the in 35 illustrated frequency response. As shown there, in the flat zone where there is no edge, the difference signal is given the frequency response shown by the solid line. As also indicated by the dashed line in 35 is shown, in the zone near an edge, the difference signal has the frequency response with which it may include a comparatively high-frequency band. Thus, the same effects as obtained by setting the size of the unsharp mask used to obtain the unsharp image signal Sus smaller than the size of the actual unsharp mask in the vicinity of an edge can be obtained. When indicated by the dashed line in 35 shown frequency response with that after 34 is compared, so the answer is in 35 low over the entire frequency band. Therefore, in the in 35 the response shown is also obtained in the flat zone that is not near an edge. The processed image signal Sproc should therefore preferably be determined using the formula (13). In the formula (13), the answer in the flat zone does not become low, and only the answer in the zone near an edge becomes small. Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = (1 / N) · {f1 (Sorg - Sus1 ) + f2 (Sorg - Sus2) + ... + fk (Sorg - Susk) + ... + fN (Sorg - SusN)} (14) where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 1 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc.

In addition, in the above-described embodiment, the dynamic range compression device according to FIG 15 the function for converting the band-limited image signal is arranged so that in In cases where the absolute value of the band-limited image signal is larger than the threshold value Th1, the absolute value may become small. Alternatively, for example, according to 18 the function for converting the band-limited image signal may be set so that in cases where the absolute value of the band-limited image signal is larger than the threshold value Th1, the absolute value may become small, such that when the absolute value of the band-limited image signal is smaller than the threshold Th2 is, the absolute value can become small.

On this way, a correction can be made in the way that if the absolute value of the band-limited image signal is smaller than the threshold value Th2, which in turn is smaller than the threshold Th1, the absolute value of the band-limited image signal in a small Value can be converted. In these cases, the answer regarding the Small absolute value components of a small signal value, which can be understood as noise within the image, be brought to a low value, consequently, noise in the processed image.

Furthermore, in the above-described embodiments of the dynamic range compression device, the processed image signal Sproc is obtained by the formula (13) or the formula (14), and as another alternative, the processed image signal Sproc can be obtained by the formula (15) given below , The formula (14) differs according to the formula (15) in the aspect explained below. In the formula (14), the band-limited image signal is obtained by subtracting each of the unsharp image signals Susk for all the frequency bands from the original image signal Sorg. On the other hand, according to formula (15), each of the blur image signals Susk having k = 2 to N is subtracted from the blur image signal Sus1. In the processing performed by the formula (14), the high-frequency components which can be regarded as noise in the image are also emphasized, and therefore noise in the resulting processed image becomes perceptible. On the contrary, in the processing according to the formula (15), the high-frequency components are eliminated. Therefore, the noise becomes imperceptible and a processed image of good image quality is obtained. Sproc = Sorg + D (Sorg-Fdrc (Sus1, Sus2, ..., SusN)) Fdrc (Sus1, Sus2, ..., SusN) = (1 / N) · {f2 (Sus1-Sus2) + f3 ( Sus1-Sus3) + ... + fk (Sus1-Susk) + ... + fN (Sus1-SusN)} (15) where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 2 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc.

As another alternative, the processing may be carried out according to the formula (16) given below. In the above formula (13), the band-limited image signal (Sorg-Sus1) is used. On the other hand, according to formula (16), the band-limited image signal (Sorg-Sus1 is not used 14 eliminated. Therefore, in the processing according to the formula (15), when the processed image signal Sproc can be formed so that the high frequency components can be eliminated, noise becomes imperceptible, and a processed image with good image quality can be obtained. Sproc = Sorg + D (Sorg-Fdrc (Sus1, Sus2, ..., SusN)) Fdrc (Sus1, Sus2, ..., SusN) = {f2 (Sus1-Sus2) + f3 (Sus2-Sus3) +. .. + fk (Susk-1-Susk) + ... + fN (SusN-1-SusN)} (16) where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 2 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc.

In the dynamic range compression process, in the frequency emphasis processing, the form of the function f should become 15 preferably be changed in accordance with the part of the object from which an image was recorded, or the frequency bands of the band-limited image signals should geän be changed.

at For example, a picture of the female breast is the change the signal value at a boundary between the background and the Skin texture sharp and clear. Therefore, when the processing is performed by eliminating the signal of the high frequency band, and only that Signal of the lower frequency band is used, it comes into existence an artifact. In cases, In which such images are processed, so should the conversion process in the Manner performed be that Signal of the high frequency band is also preserved. At a Chest image, there is no sharp or strong signal change as in mammography. Even if the signal of the high frequency components not in the conversion process is preserved, it does not come to the mammographic image to a Artifact. In such cases, in which, for example, the bust image is processed, too only the signal of the low-frequency components undergo the conversion process become. In cases, in which the processing is performed such that the absolute values the band-limited image signals corresponding to the frequency bands of band-limited image signals or the portion of the object from which the picture was recorded, changed can be can. a picture of good picture quality according to the object section recorded as a picture or correspondingly the frequency bands to be obtained.

The conversion of the band-limited image signals within the converter 3 can be made regardless of the above-mentioned threshold value Th1 and corresponding to a plurality of functions f1 to fN which vary for different frequency bands, so that the absolute value of each band-limited image signal can be converted into a value not larger than the absolute value of the band-limited image signal, and which is determined in accordance with the absolute value of the band-limited image signal.

What the functions f1 to fN, so should preferably a combination matching functions according to the characteristic to be achieved Characteristics of image processing are set up.

In the embodiment without the threshold, the processed image signal Sproc may be adjusted to have an arbitrary frequency response, for example, by varying the functions included in the converter 3 used for different frequency bands. As for the frequency emphasis processing, therefore, the frequency response of the processed image signal Sproc can be adjusted according to the conditions required for the images to be processed in the respective apparatuses described above.

In the cases of the dynamic range compression process, functions varying at different frequency bands, for example, those in 36 used functions shown. Each of the functions after 36 converts a band-limited image signal so that the absolute value of the band-limited image signal can be converted into a value not larger than the absolute value of the band-limited image signal and determined according to this absolute value. In addition, each of the functions performs the conversion such that for the band-limited image signal whose absolute value is larger than the predetermined value, the value of the converted image signal becomes about a predetermined level. As in 36 In a function for processing a lower frequency band, the aforementioned predetermined value is set smaller than the values of the other functions.

In In other words, the functions have such courses that every function can go through the origin, hence the inclination of the function at most 1 is adjustable irrespective of the value of the band-limited image signal, which has been processed with the function, wherein also the Slope of the function becomes 0 or approaches the value 0 if the absolute value of the signal value of the function processed with the function bandlimited image signal becomes large. Furthermore the functions have such a history as to be processed by a function a low-frequency band the absolute value of the processed Signal value, which belongs to the point of the function, in which the slope of the function is 0 or a value close to 0 has a smaller value than the other functions. The functions have such an effect that the edge information as much as possible maintained in a narrow dynamic range.

Effects that are achieved by varying the functions for different frequency bands are described below. 19A . 19B . 19C and 19D FIG. 4 are illustrative views of the problems associated with the conventional method in which the same function is used for the functions f1 to fN, that is, the band-limited image signals of all the frequency bands are converted by the same conversion method. In the 19A . 19B . 19C and 19D For example, the processing near an edge where the image density changes abruptly is stepwise displayed. 19A shows a step-like profile of the original image signal and the profiles of the blur image signals formed from the original image signal. 19B shows the profiles of the band-limited image signals for the in 19A represented signals. 19C shows the profiles of the converted image signals, and 19D shows the profile of the signal obtained by integrating the converted signals.

There is a possibility that in 18 to use functions that are also used in the frequency emphasis processing. These functions have the effect that the increase of the integration signal according to 19D which has been obtained by integrating the converted image signals can be made natural. In this way one obtains a smooth processed image signal Sproc.

Farther can Functions, both characteristics of the two types described above Have functions used to thereby reduce the effects of both To get functions. In such cases, those for the individual Frequency bands used Functions according to the characteristics to be achieved Image processing can be varied, the frequency response for the whole Frequency band be adjusted accordingly.

In the following, another embodiment of the dynamic range compression method and the corresponding apparatus according to the invention will be explained. In this embodiment, those of the blur image signal generation means are 1 , the bandlimited image signal generating means 2 , the integration facility 4 , the subtractor 6 and the compression process device 7 Processes performed are the same as those associated with above 28 described embodiment. For this reason, only that of the conversion device 3 performed process will be described below.

Basically, the structure of the embodiment of the dynamic range compression device to be described below according to 20 illustrate the image processing apparatus for performing the frequency emphasis processing described above. However, different are in the surgical facility 23 built-in devices other than those used for the frequency emphasis processing. The following is based on the 20 the embodiment of the dynamic range compression device explained.

As in the embodiment explained above, the band-limited image signals are formed from the original image signal Sorg and from the plurality of unsharp image signals Susk received from the unsharp image signal generating means 1 were generated. Each of the bandlimited image signals is from the subtractor 21 obtained subtracting the blur image signals Susk two adjacent frequency bands (for the original image signal Sorg are Sorg and Sus1) of each other. Specifically, the values of Sorg-Sus1, Sus1-Sus2, ... SusN1-SusN are successively calculated to thereby obtain the band-limited image signals. In this embodiment, for the band-limited image signal Sus1-Sus2, for example, the signal Sus2-Sus3 is used as the associated auxiliary image signal. Therefore, a single device serves essentially as a band-limited image signal generating means and as an auxiliary image signal generating means. In particular, the signal generated by the band-limited image signal generating means is processed as a band-limited signal and also as an auxiliary image signal corresponding to the adjacent band-limited image signal.

Thereafter, each band-limited image signal obtained in this way is converted in the conversion means 3 transformed. The conversion is done for each band-limited image signal by using the converter 22 and the converter 24 according to 20 , In the converter 22 For example, the conversion process is performed using one of the functions f1 to fN of the respective frequency bands to obtain a limited or limited image signal. In the converter 24 a conversion process is performed by using a function g to thereby obtain a magnification ratio signal. In the multiplier 25 the limited image signal is multiplied by the enlargement ratio signal. In this way, the conversion takes place. In such cases, according to 20 multiplies the signals of two adjacent frequency bands. The limited image signal obtained by converting the band-limited image signal Sus1-Sus2 and the enlargement ratio signal obtained by converting the auxiliary image signal Sus2-Sus3 are multiplied together, for example. The converter 22 for performing the conversion with the function fk corresponds to a restricted image signal generating means. The converter 24 for performing the conversion with the function g corresponds to the enlargement ratio signal generating means. Furthermore, the multiplier corresponds 25 to multiply that of the converters 22 and 24 formed output signals together a multip likationseinrichtung or multiplier.

The functions f1 to fN may be identical to each other, but they may be different. They can be arbitrarily set according to the characteristics of the image processing to be performed. In this embodiment, as functions f1 to fN, those functions are used each of which limits the band-limited image signal so as to become smaller than the absolute value of the band-limited image signal, and the functions are also different for different frequency bands. For example, the in 36 used functions shown.

As for the frequency emphasis processing, for example, for the function g, the in 21 used function shown.

As a function g, for example, a function as described in FIG 21 is shown. With the function g according to 21 For example, the auxiliary image signal is converted so that a converted value close to 1 can be obtained when the absolute value of the auxiliary image signal is small, while when the absolute value of the auxiliary image signal becomes large, the converted value can be close to 0. In 21 K is the minimum value of the absolute value of the auxiliary image signal among the values for which the converted values become 0.

The converted image signals transmitted by the converter 3 are received in the operation facility 23 fed with the integrator 4 , the subtractor 6 and the compression process device 7 Is provided. In the operation facility 23 The following explained processing is performed. First, the plurality of converted image signals are integrated to obtain an integration signal, which is then subtracted from the original image signal Sorg to form a difference signal. Subsequently, the dynamic range compression factor, which is obtained with the function D and takes a value corresponding to the value of the difference signal, is added to the original image signal Sorg to obtain the processed image signal Sproc. The above processing can be represented by the following formula (17): Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg - Sus1} · g (Sus1 - Sus2) + f2 (Sus1-Sus2) * g (Sus2-Sus3) + ... + fk (Susk-1-Susk) * g (Susk-Susk + 1) + ... + fN (SusN-1-SusN ) · G (SusN - SusN + 1)} (17) where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N + 1) means the unsharp image signal, fk (k = 1 to N) is the function of converting the band-limited one Means that the function for converting the auxiliary image signal and obtaining the enlargement ratio signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D is the function for converting from Sorg - Fdrc means.

22A . 22B . 22C . 22D and 22E show the effects that are achieved when an image signal corresponding to a zone in the vicinity of an image edge with the embodiment after 20 is processed. 22A also shows the profiles of the blur image signals generated from the original image signal, according to the zone near an image edge. 22B shows the profile of the band-limited image signal with respect to in 22A represented signals. 22C shows the profile of the auxiliary image signal containing the signal of the frequency band which is one level lower than the frequency band of 22B shown bandlimited image signal. 22D shows the profile of the enlargement ratio signal which is obtained when the in 22C represented auxiliary image signal is processed with the function g. 22E FIG. 12 shows the profile of the processed signal which is obtained when the limited image signal obtained by processing the band-limited image signal. FIG 22B with the function f, with the enlargement ratio signal after 37D is multiplied. The value K in 37C is identical to the value K in 36 , It is shown that when the value of the auxiliary image signal after 37C takes the value K, the value of the enlargement ratio signal after 37D 0 becomes. When the band-limited image signal goes to 37B is only converted so that its absolute value becomes small, the shape of the peak of the signal smoothes, but the rising part of the signal remains steep. On the other hand, in the in 37E shown processed signal of the rising part smooth. In cases where the rising part of each band-limited signal is smoothed, a step-like artifact at the boundaries among the frequency bands in the signal can be avoided, which is obtained by integrating the band-limited signals.

With the above explained Processing can be done as in frequency emphasis processing Smoothing portion of each band-limited image signal be smoothed and the appearance of a step-like Artifacts at the boundaries between the frequency bands in the signal, which is obtained by integrating the band-limited image signals, let yourself prevent.

The auxiliary image signals processed by the function g are not limited to the above cases. For example, the dynamic range compression process may be performed with formula (18) below: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = (f1 (Sorg - Sus1) · g (Sorg - Sus2) + f2 (Sus1 - Sus2) · g (Sorg - Sus3) + ... + fk (Susk - 1 - Susk) · g (Sorg - Susk + 1) + ... + fN (SusN - 1 - SusN ) · G (Sorg - SusN + 1)} (18) where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N + 1) means the unsharp image signal, fk (k = 1 to N) is the function of converting the band-limited one Means that the function for converting the auxiliary image signal and obtaining the enlargement ratio signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D is the function for converting from Sorg - Fdrc means.

at the embodiment, which makes use of the formula (18) becomes an image signal which a zone nearby corresponds to an image edge, processed by the conversion in terms of The signal received at the edge has a contrast level that is smaller is as the signal, which by converting the edge with low Contrast level is obtained.

23A-1 . 23B-1 . 23C-1 . 23D-1 and 23E-1 FIG. 15 shows the effects that occur when an image signal corresponding to a zone near a high contrast level image edge is processed in the embodiment using the formula (11). 23A-1 Fig. 12 shows a step-like profile of an original image signal corresponding to the zone near the edge of the image, and the profiles of the blur image signals generated from the original image signal. 23B-1 shows the profile of the band-limited image signal with respect to the in 38A-1 represented signals. 38C-1 shows the profile of the auxiliary image signal containing the signal of the frequency band having a lower signal level than the frequency band of the band-limited signal according to FIG 38B-1 , 38D-1 shows the profile of the enlargement ratio signal which is obtained when the in 38C-1 represented auxiliary image signal is processed with the function g. 38E-1 FIG. 12 shows the profile of the processed image signal obtained when the limited image signal obtained by processing the bandlimited signal. FIG 38B-1 with the function f is obtained with the in 38D-1 is multiplied shown magnification ratio signal. 38A-2 . 38B-2 . 38C-2 . 38D-2 and 38E-2 show the effects obtained when an image signal corresponding to a zone near a high-contrast image edge is processed by the formula (11) in the embodiment. 38A-2 . 38B-2 . 38C-2 . 38D-2 and 38E-2 are similar views like that 38A-1 . 38B-1 . 38C-1 . 38D-1 respectively. 38E-1 , As shown in these figures, in cases where the value of Sorg-Susk is used as a value to be processed with the function g, the signal obtained by the conversion for the high contrast level edge becomes small. As for the edge with high contrast level, the signal obtained by the conversion has a level similar to the level of the original band-limited image signal.

The signals obtained by the conversion are then integrated, and the integration signal thus obtained is extracted from the original image signal Sorg subtracted. Subsequently is the dynamic range compression factor associated with the function d, and a value corresponding to the value of the thus obtained Difference signal, added to the original image signal sorg. In such cases can be achieved effects where the influence of the edge with high contrast level to the processed image signal smaller is considered the influence of Edge with low contrast level on the processed image signal.

The Both types of formulas have the effects described above. There are various modifications to the functions f and g and re the formation of the band-limited to be processed with the function g Picture signal possible.

Another other embodiment of the dynamic range compression method and the dynamic range compression device according to the invention will be described below. In this embodiment, the processes performed by the blur image signal generator 1 , the bandlimited image signal generating means 2 , the integrator 4 , the subtractor 6 and the compression process device 7 be carried out the same as in the above in connection with 28 described embodiment. Therefore, in the following, only the conversion means 3 executed process described.

Basically, the structure of the embodiment of the dynamic range compression device described below can be found in FIG 24 illustrated way. However, different are in the surgical facility 23 built-in devices other than those for the frequency emphasis processing. The following is based on the 4 and 20 the embodiment of the dynamic range compression device will be explained.

As in the above embodiment, the band-limited image signals are obtained from the original image signal Sorg and the plurality of unsharp image signals Susk, which in turn are obtained by means of the blur image signal generator 1 were formed. Each of the bandlimited image signals is from the subtractor 21 obtained subtracting the blur image signals Susk two adjacent frequency bands (for the original image signal sorg Sorg and Sus1) from each other. Specifically, the values Sorg-Sus1, Sus1-Sus2, ... SusN-1-SusN are successively calculated to thereby obtain a plurality of band-limited image signals.

Thereafter, each of the thus obtained band-limited image signals in the conversion means 3 transformed. As in 24 are shown in the converter 3 a band-limited signal to be converted in a certain frequency band, and a signal (that is, an auxiliary image signal) having a function g in by converting a low-frequency band-limited signal of a frequency band one step below the band-limited frequency band to be converted the converter 24 is obtained, added up. In this way, a composite band-limited image signal is obtained, and this signal obtained by the addition is then stored in the converter 22 converted with a function fk. The converter 24 for performing the conversion with the function g corresponds to the auxiliary image signal generating means, and the adder 26 corresponds to composite band-limited image signal generating means.

As the function g, for example, the in 25 shown function can be used. In the dynamic range compression process, like the frequency emphasis processing, that part of this function in which the slope gradually increases from the origin gradually detracts from the waveform in the rising part of the auxiliary image signal. Especially in cases where the conversion is performed with this function, a steep rise area can be smoothed. As with the frequency emphasis processing, as functions g, any functions may be used whose slope gradually increases from 0 in the zone near the origin. For example, the in 26 shown function can be used.

The functions f1 to fN used in this embodiment may be the same or different from each other. They can be set arbitrarily according to the characteristics of the image processing to be performed. In this embodiment, as functions f1 to fN, for example, those in FIG 36 used functions shown.

That of the converter 3 The converted image signals obtained are transferred to the operation device 23 entered with the integration device 4 , the subtractor 6 and the compression process device 7 Is provided. In the operation facility 23 the processing is carried out in the manner explained below. First, the plurality of converted image signals are integrated to obtain an integration signal, which is then subtracted from the original image signal Sorg to form a difference signal. Thereafter, the difference signal is converted, and the signal thus obtained is added to the original image signal Sorg. In this way one obtains the processed image signal Sproc. This process and the other processes described above can be represented by the following formula (19): Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = [f1 {(Sorg - Sus1) + g (Sus1 - Sus2)} + f2 {(Sus1 - Sus2) + g (Sus2 - Sus3)} + ... + fk {(Susk - 1 - Susk) + g (Susk - Susk + 1)} + ... + fN {(SusN-1-SusN) + g (SusN-SusN + 1)}] (19) where Sproc means the image signal whose dynamic range has been compressed, the original image signal means, Susk (k = 1 to N + 1) represents the unsharp mask image signal, fk (k = 1 to N) means the composite band-limited image signal converting function , g denotes the function of converting the band-limited image signal and obtaining the auxiliary image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D represents the function of converting Sorg-Fdrc.

27A . 27B . 27C . 27D . 27E and 27F show the effects obtained when an image signal corresponding to a zone in the vicinity of an image edge with the embodiment according to 24 is processed. 27A also shows the profiles of the blur image signals generated from the original image signal, according to the zone near an image edge. 27B and 27C showed the profiles of the band-limited image signals with respect to the in 27A shown signals. In cases where the in 27B The band-limited image signal shown as the band-limited image signal to be converted is taken as shown in FIG 27C shown band-limited image signal taken as located on the low-frequency side band-limited image signal. 27D shows the profile of the auxiliary image signal, which is obtained when the in 27C shown on the low-frequency side band-limited image signal is processed with the function g. 27E Fig. 12 shows the profile of the composite bandlimited image signal obtained by adding the band-limited image signal to be converted 27B and the auxiliary picture signal, which is in 27D is shown. 27F FIG. 12 shows the profile of the converted image signal obtained by processing the composite bandlimited image signal. FIG 27E with the function f. If that is in 27B The band-limited image signal shown is only converted so that its absolute value becomes small, the shape of the signal peak is smooth, however, the rising range of the signal can be kept steep. On the other hand, in the converted image signal after 27F its rise part is smooth. In cases where the rising part of each band-limited image signal is smoothed in this way, occurrence of a stepped artifact at the boundaries between the frequency bands in the signal obtained by integrating the band-limited image signals can be avoided.

With The processing described above may be the same as the frequency emphasis processing the ramp portion of each bandlimited signal is smoothed. Furthermore may be the occurrence of an artifact in the processed image signal due to the presence of a step-like pattern at the boundaries between the frequency bands in the signal obtained by integrating the band-limited signals won, be prevented.

at this embodiment The dynamic range compression device are various modifications possible, for example the functions f and g.

at the embodiments described above the dynamic range compression process will help with the blur image signals Susk executed. Alternatively, together with the dynamic range compression process, the above described frequency emphasis processing with respect to the original image signal Sorg using the blur image signals Susk performed become.

In such cases, in which the dynamic range compression process and the frequency emphasis processing executed simultaneously can, can the blur image signals Susk be used together. Therefore, the time for performing the Shorten operations, and the processing can be performed more efficiently than when the dynamic range compression process and the Frequency emphasis processing would be carried out independently.

Also, in the cases where the dynamic range compression and the frequency emphasis are executed simultaneously, the influence of the comparatively low frequency components of the original image in the dynamic range compression process is large, and the influence of the comparatively high frequency components of the original image is large in the frequency emphasis processing. In such cases, where the dynamic range compression is performed for the plurality of unsharp image signals Susk, the Blur image signals Susk the comparatively low-frequency bands are used. Even in cases where the frequency emphasis is performed, the blur image signals Susk of the comparatively high-frequency frequency bands can be used. In this way, the blur image signals Susk different frequency bands can be used in the dynamic range compression and frequency emphasis.

at the above-described embodiments of the multi-resolution transformation method, the Frequency emphasis processing and the dynamic range compression method the interpolation process gets under Using the filter of the Gauss signal performed. alternative can interpolate the filtered image using B-spline interpolation carried out become. Like the B-spline interpolation carried out will be explained below become.

The B-spline interpolation is an interpolation method of obtaining an interpolation image signal representing a secondary image in which the sharpness is comparatively small, and which is smooth. In B-spline interpolation, the spline interpolation function need not pass through the original sample points (pixels), it is necessary that the first order differential quotient and the second order differential quotient (denoted f "(X)) of the spline interpolation function between adjacent sections are steady. In particular, the following formula (20) must be satisfied: f k (x) = A k x 3 + B k x 2 + C k x + D k (20) where B _{k is} a coefficient which is to facilitate the explanation, but does not represent the filtered image signal, and the following conditions should also be satisfied: f k '(X k ) = f k-1 '(X k ) (21) f k '(X k + 1 ) = f k + 1 '(X k + 1 ) (22) f k '' (X k ) = f k-1 '' (X k ) (23) f k '' (X k + 1 ) = f k + 1 '' (X k + 1 ) (24)

In addition, it is necessary that the first order differential quotient on the picture element X _{k satisfy} the condition with respect to the picture elements X _k-1 and X _{k + 1} located in front of and behind the picture element X _k , after which the first order differential quotient on the picture element X _k coincides with that Gradients (Y _{k + 1} -Y _k-1 ) / (X _{k + 1} -X _k-1 ) of the image signal components Y _k-1 and Y _{k + 1} for the picture elements X _k-1 and X _{k + 1} . Therefore, the following formula (25) must be satisfied. fk '(X k ) = (Y k + 1 - Y k-1 ) / (X k + 1 - X k-1 ) (25)

Furthermore, the differential quotient of the first order in the picture element X _{k + 1} for the picture elements X _k and X _{k + 1} before and after the picture element X _{k + 1} must satisfy the condition that the first order differential quotient on the picture element X _{k + 1} coincides with the gradient (Y _{k + 2} - Y _k ) / (X _{k + 2} - X _k ) satisfies the image signal components Y _k and Y _{k + 1} for the picture elements X _k and X _{k + 2} , respectively. Therefore, the following formula (26) must be satisfied: fk '(X k + 1 ) = (Y k + 2 - Y k ) / (X k + 2 - X k ) (26)

In general, the function f (X) can be approximately represented by the formula (27): f (X) = f (0) + f '(0) X + {f''(0) / 2} X 2 (27)

Here, suppose that the interval (that is, the lattice interval) of each of the sections X _k-2 ~ X _k-1 , X _k-1 ~ X _k , X _k ~ X _{k + 1} and X _{k + 1} ~ X _{k + 2 has} the value 1 and the position of the interpolation point X _p which is from the picture element X _k in the direction of the picture element X _{k + 1} is represented by t (0 ≦ t ≦ 1). In these cases one obtains from the formulas (21), (22), (23), (24) and (27): fk '(0) = C k = (Y k + 1 - Y k-1 ) / 2 fk '(1) = 3A k + 2B k + C k = (Y k + 2 - Y k ) / 2 fk '' (0) = Y k + 1 - 2Y k + Y k-1 = 2B

So you get the following formulas: 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, it can be represented by the following formula: D k = (D 1 Y k + 2 + D 2 Y k + 1 + D 3 Y k + D 4 Y k-1 ) / 6

As described above, the variable conversion of X = t is performed, and therefore, the spline interpolation function f _k (x) can be represented by the following formula: 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

If this formula is ordered according to the image signal components Y _k-1 , Y _k , Y _{k + 1} and Y _{k + 2} , formula (28) results: 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 (28)

If you set t = 1, you get the following formula: 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

For the section X _{k + 1} ~ X _{k + 2} one obtains the formula (29): 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 (29)

If t = 0 is set, the following formula is obtained: f k + 1 (0) = (D 4 / 6) Y k + (D. 3 / 6) Y k + 1 + (D. 2 / 6) Y k + 2 + (D. 1 / 6} Y k + 3

From the continuity condition {f _k (1) = f _{k + 1} (0)} and the condition that the coefficients corresponding to the individual filtered image signal components are equal to each other, D ₄ - 1 = 0, D ₃ - 3 = D ₄ , D ₂ + 3 = D ₃ , D ₁ + 1 = D ₂ and D ₁ = 0 gives: D k = (Y k + 1 + 4Y k + Y k-1 ) / 6

This gives the formula (30): 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 (30)

The interpolation _coefficients b _k-1 , b _k , b _{k + 1} and b _{k + 2} which correspond to the filtered image signal components Y _k-1 , Y _k , Y _{k + 1} and Y _{k + 2} can be represented by the following formulas : 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 b k + 2 = t 3 / 6

The above-described operations are repeated for the sections _Xk-2 ~ _Xk-1 , _Xk-1 ~ _Xk , _Xk ~ _{Xk + 1} and _{Xk + 1} ~ _{Xk + 2} . In this way, an interpolation image signal consisting of image signal components appearing at intervals different from those of the image signal components of the entire filtered image signal can be obtained.

In such cases, in which the B-spline interpolation with respect to each of the filtered Image signals Bk performed becomes, so let the blur image signals Detect Susk corresponding to the filtered image signals Bk.

Also in the embodiments described above is the filter processing regarding the image signal components of the original image signal Sorg, which represent the picture elements, in every second row and in every second column of the Field of picture elements of the original image. Subsequently, will the interpolation made. The process is carried out successively, and this will make the blur image signals Susk received. Alternatively it can be filtering the original image signal Sorg using several unsharp masks different size to perform in this way the blur image signals Susk with different frequency responses to obtain.

Besides that is the frequency emphasis processing or the dynamic range compression process of the above embodiments also applicable if the frequency emphasis or the dynamic range compression at a flow pattern carried out becomes, as it appears in a picture, that in a Tomographiebildaufnahme arises. The embodiment, at the frequency emphasis and the dynamic range compression performed simultaneously is also applicable to a picture taken by tomography record is obtained. In this way can be the flow pattern eliminate, and the emergence of an artifact in the zone in the Near one Image edge can be avoid.

Especially is the image processing method for eliminating the flow pattern, which occurs when taking a tomographic image recording is done by performing the image processing with blur masks, For example, in Japanese Unexamined Patent Publication No. 3 (1991) -276265. In such cases, the problem arises that it in the zone of an edge where the image density abruptly changes an artifact comes. In such cases, the flow pattern eliminate the blur image signals Susk won with the help of a one-dimensional blur mask. Especially then when the blur image signals with different frequency characteristics in the above embodiments can be obtained filtering with the one-dimensional blur mask for removing the flow pattern be made. Then band-limited image signals can be correspondingly win the filtered image signals thus obtained. If the absolute value each band-limited image signal is comparatively large, can the band-limited image signal is converted so that it has a gets small absolute value. Then the subsequent processes carried out, around the flow pattern to eliminate, so that a Image can be obtained, which is free of artifacts and good picture quality has.

In addition, together with the process using the one-dimensional defocus mask for eliminating the flow pattern, the above-explained process using the two-dimensional defocus mask, or the process in which the one-dimensional defocus mask processing is performed in two directions, can be performed. In such cases, the flow pattern can be eliminated from the original image, and the appearance of the artifact in the region near an edge can still be seen suppress it. For this reason, a processed image of good quality can be obtained.

As explained above In the frequency emphasis processing and in the dynamic range compression process, with respect to the Bandlimited image signals executed conversion process for various Sections or areas of the object from which images are recorded were varied. The idea, the process according to the section is the object from which an image recording was made to every embodiment applicable.

Claims (36)

Method for dynamic range compression, at which is a signal regarding low-frequency components of an original image to an original image signal, which represents the original picture, and the dynamic range of the original image is thereby added is compressed, comprising the following steps: i) from the original image signal become multiple blur masks image signals generated, which have different frequency responses, ii) from the original image signal and the plurality of unsharp mask image signals or from the plurality of unsharp mask image signals a plurality of band-limited image signals are formed, each of which represents one of the signals, in several different frequency bands of the original image signal fall, iii) Perform a conversion process regarding at least one single band-limited image signal among the plurality band-limited image signals in such a way that at least part of the band-limited Picture signal can become small, whereby a plurality of converted Image signals, iv) Integrate the several converted ones Image signals to produce an integration signal, v) Subtract the integration signal from the original image signal, thereby to To obtain difference signal, and vi) converting the difference signal, thereby the signal with respect to to gain the low-frequency components of the original image, which signal is to be added to the original image signal. Method according to claim 1, wherein the formation of the a plurality of converted image signals is effected in that a bandlimited Image signal with a signal value whose absolute value is greater is converted as a predetermined threshold such that the absolute value the signal value of the band-limited signal with the absolute value greater than the predetermined threshold value is converted to a small value can. Method according to claim 2, wherein the formation of the a plurality of converted image signals such that a band-limited Signal is converted so that when the absolute value of the signal value of the band-limited signal greater than the predetermined threshold becomes, the absolute value of the signal value of band-limited signal to be converted into a small value can, and in such a way that, when the absolute value of the signal value of the band-limited image signal becomes smaller than another threshold smaller than that predetermined threshold is the absolute value of the signal value of the band-limited image signal is converted into a small value can be. A method according to claim 2 or 3, wherein the formation the plurality of converted image signals is performed in such a way that a band-limited Signal is converted so that the Level of the absolute value of the signal value of the band-limited signal in accordance with the frequency band of the band-limited image signal to be changed can. Method according to claim 1, wherein the formation of the a plurality of converted image signals is performed in the manner the existence band-limited signal as specified one of several functions is being converted for different frequency bands vary so that the Absolute value of the signal value of the band-limited image signal in one Value that is not greater than the absolute value and as required of the absolute value. Method according to Claim 5, in which each of the functions, used to form the converted image signals, a band-limited image signal with a signal value whose absolute value bigger than one is predetermined value, converted such that the value of the converted Approximated image signal can become equal to a predetermined level, the functions be set so that in a function for processing a high-frequency band of the predetermined Value is set smaller than the values in other functions. Method according to claim 5 or 6, wherein the functions necessary for the formation of the converted image signals are set so that, in a function for processing a low frequency band, the absolute value of the converted picture signal obtained when the band-limited picture signal is converted to a signal value whose absolute value falls within a predetermined range near 0 is converted , can take a value smaller than the values in the other functions. A method according to claim 1, 2 or 5, wherein the formation of the band-limited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low-frequency components and the addition of the signal with respect to the low-frequency components to the original image signal according to the following formula: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg - Sus1) + f2 (Sus1 - Sus2) + ... + fk (Susk-1-Susk) + ... + fN (SusN-1-SusN)} where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 1 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. A method according to claim 1, 2 or 5, wherein the formation of the band-limited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low-frequency components and the addition of the signal with respect to the low-frequency components to the original image signal according to the following formula: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = (1 / N) · {f1 (Sorg - Sus1 ) + f2 (Sorg - Sus2) + ... + fk (Sorg - Susk) + ... + fN (Sorg - SusN)} where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 1 to N) has the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. A method according to claim 1 or 2, wherein the formation of the band-limited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low-frequency components and the addition of the signal with respect to the low-frequency components to the Original image signal to be executed according to the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sus1, Sus2, ..., SusN)) Fdrc (Sus1, Sus2, ..., SusN) = {f2 (Sus1-Sus2) + f3 (Sus2-Sus3) +. .. + fk (Susk-1-Susk) + ... + fN (SusN-1-SusN)} where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 2 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. The method of claim 1 or 2, wherein forming the bandlimited image signals, forming the converted image signals, forming the integration signal, forming the difference signal, forming the signal with respect to the low frequency components, and adding the signal with respect to the low frequency components the original image signal is executed according to the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sus1, Sus2, ..., SusN)) Fdrc (Sus1, Sus2, ..., SusN) = (1 / N) · {f2 (Sus1-Sus2) + f3 ( Sus1-Sus3) + ... + fk (Sus1-Susk) + ... + fN (Sus1-SusN)} where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N) means the unsharp image signal, fk (k = 2 to N) represents the function of converting the band-limited image signal and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. Method according to claim 1, wherein the formation of the several converted image signals is performed as follows: Convert a band-limited image signal in such a way that the absolute value the signal value of the band-limited image signal is converted into a value can be, which is not bigger as the absolute value and determined according to the absolute value, in this way a limited To gain image signal, Forming an auxiliary picture signal which a signal with a frequency band below the frequency band of the band-limited Contains image signal, which uses to form the limited image signal was, with the auxiliary image signal from the original image signal and several of the unsharp mask image signal is produced, Converting the auxiliary image signal in the way that if the absolute value of the signal value of the auxiliary image signal becomes small, the absolute value can be converted into a value close to 1 can, and in such a way that, when the absolute value of the signal value of the auxiliary image signal becomes large, the absolute value can be converted into a value close to 0 can thus obtain a magnification ratio signal the restricted Image signal corresponds, and Multiplying the restricted image signal with the multiplication ratio signal, which the restricted Image signal corresponds. A method according to claim 1 or 12, wherein the formation of the bandlimited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low frequency components, and the addition of the signal with respect to the low frequency components are applied to the Original image signal to be carried out according to the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg-Sus1) * g (Sus1 - Sus2) + f2 (Sus1-Sus2) * g (Sus2-Sus3) + ... + fk (Susk-1-Susk) * g (Susk-Susk + 1) + ... + fN (SusN-1-SusN ) · G (SusN - SusN + 1)} where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N + 1) means the unsharp image signal, fk (k = 1 to N) is the function of converting the band-limited one Means that the function for converting the auxiliary image signal and obtaining the enlargement ratio signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D is the function for Transforming Sorg - Fdrc means. A method according to claim 1 or 12, wherein the formation of the bandlimited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low frequency components, and the addition of the signal with respect to the low frequency components are applied to the Original image signal to be carried out according to the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg-Sus1) * g (Sus1 - Sus2) + f2 (Sus1 - Sus2) · g (Sorg - Sus3) + ... + fk (Susk - 1 - Susk) · g (Sorg - Susk + 1) + ... + fN (SusN - 1 - SusN ) · G (Sorg - SusN + 1)} where Sproc stands for the image signal in which the dynamic range has been compressed, since the original image signal means that Susk (k = 1 to N + 1) means the unsharp image signal, fk (k = 1 to N) is the function of converting the band-limited one Means that the function for converting the auxiliary image signal and obtaining the enlargement ratio signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D is the function for converting from Sorg - Fdrc means. Method according to claim 1, wherein the formation of the several converted image signals is performed as follows: Convert a band-limited on the low-frequency side An image signal belonging to a frequency band below the frequency band a bandlimited, to be converted image signal is, with a non-linear function to thereby generate an auxiliary picture signal for the one to be converted to obtain band-limited image signal, using the non-linear function such a characteristic has that the function by a Origin, therefore, the slope of the function in the origin approximately can assume the value 0, and such that the slope of the function gradually may increase as the value being processed increases, Add the auxiliary image signal to the band-limited image signal to be converted, to thereby obtain a composite band-limited image signal, and Converting the composite bandlimited image signal in such a way that the Absolute value of the signal value of the composite band limited Image signal can be converted to a value that is not larger as the absolute value, and determined according to the absolute value. A method according to claim 1 or 15, wherein the formation of the band-limited signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low-frequency components and the addition of the signal with respect to the low-frequency components to the Original image signal to be carried out according to the following formula: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = [f1 {(Sorg - Sus1) + g (Sus1 - Sus2)} + f2 {(Sus1 - Sus2) + g (Sus2 - Sus3)} + ... + fk {(Susk - 1 - Susk) + g (Susk - Susk + 1)} + ... + fN {(SusN-1-SusN) + g (SusN-SusN + 1)}] where Sproc means the image signal whose dynamic range has been compressed, the original image signal means, Susk (k = 1 to N + 1) represents the unsharp mask image signal, fk (k = 1 to N) means the composite band-limited image signal converting function , g represents the function of converting the band-limited image signal and obtaining the auxiliary image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D represents the function of converting Sorg-Fdrc. A method according to any one of claims 1, 2, 5 and 8 to 16, at which causes the formation of the plurality of converted image signals thereby the existence band-limited image signal according to the part an object whose image is recorded as an original image has been. A method according to any one of claims 1, 2, 5 and 8 to 17, at the original image by a tomographic image recording was won and a flow pattern gets thereby forming the plurality of unsharp mask image signals done that one Unsharp mask processing in terms of of the original image signal using a one-dimensional unsharp mask carried out which extends in the direction of the flow pattern. A dynamic range compression device in which a signal relating to low frequency components of an original image is added to an original image signal representing the original image and the dynamic range of the original image is compressed thereby, comprising: i) a blur mask image signal generating means for forming a plurality of unsharp masks Ii) a bandlimited image signal generating means for forming a plurality of bandlimited image signals each of which represents one of the signals falling in a plurality of different frequency bands of the original image signal from the original image signal and the plurality of unsharp mask image signals; or off iii) conversion means for performing conversion of at least a single bandlimited image signal among the plurality of bandlimited image signals such that at least a portion of the bandlimited image signal may become small, to thereby obtain a plurality of converted image signals, iv) a plurality of unsharp mask image signals; Integrating means for integrating the plurality of converted image signals to thereby obtain an integration signal; v) subtracting means for subtracting the integration signal from the original image signal to thereby obtain a difference signal, and vi) compressing means for converting the difference signal to thereby express the signal with respect to Obtain low-frequency components of the original image, which is to be added to the original image signal. Apparatus according to claim 19, wherein the conversion means generates the plurality of converted image signals by being on band-limited image signal having a signal value whose absolute value is greater than is a predetermined threshold value, converted such that the absolute value the signal value of the band-limited image signal, which absolute value greater than the predetermined threshold is converted to a small value can be. Apparatus according to claim 20, wherein the conversion means the plurality of converted image signals by converting a band limited one Image signal is generated in such a way that when the absolute value of the Signal value of the band-limited image signal greater than the predetermined threshold value becomes, the absolute value of the signal value of the band-limited image signal can be converted to a small value, and such that when the absolute value of the signal value of the band-limited image signal becomes smaller as another threshold smaller than the predetermined one Threshold is the absolute value of the signal value of the band limited Image signal can be converted to a small value. Apparatus according to claim 20 or 21, wherein the Conversion means the plurality of converted image signals thereby that creates them converts a band-limited image signal such that the level of the Absolute value of the signal value of the band-limited image signal after proviso the frequency band of the band-limited image signal to be changed can. Apparatus according to claim 19, wherein the conversion means forming the plurality of converted image signals by being on converts band-limited image signal according to one of several functions, the for different frequency bands vary so that the Absolute value of the signal value of the band-limited image signal in one Value can be converted, which is not greater than the absolute value and determined according to the absolute value. Apparatus according to claim 23, wherein each of the functions, used to form the converted image signals, a band-limited signal value with a signal value, its absolute value greater than is a predetermined value, converted such that the value of the converted Image signal approximately corresponds to a predetermined level, the functions being such be set up that in a function for processing a high-frequency band of the predetermined Value is set smaller than that in the other functions. Apparatus according to claim 23 or 24, wherein the Functions used to form the converted image signals be set so that in a function for processing of a low frequency band the absolute value of the converted Image signal obtained when the band-limited image signal has a signal value whose absolute value is in a predetermined Area nearby falls from 0, is converted, a value smaller than those in the other functions can accept. Apparatus according to claim 19, 20 or 23, wherein the formation of the band-limited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low-frequency components and the addition of the Si signal with respect to the low-frequency Components to the original image signal using the following formula: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg - Sus1) + f2 (Sus1 - Sus2) + ... + fk (Susk-1-Susk) + ... + fN (SusN-1-SusN)} where Sproc means the image signal whose dynamic range has been compressed, Sorg is the original image signal, Susk (k = 1 to N) means the unsharp mask image signal, fk (k = 1 to N) means the function of converting the band-limited image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. Apparatus according to claim 19, 20 or 23, wherein forming the bandlimited image signals, forming the converted image signals, forming the integration signal, forming the difference signal, forming the signal with respect to the low frequency components, and adding the signal to the low frequency components to the original image signal using the following formula: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = (1 / N) · {f1 (Sorg - Sus1 ) + f2 (Sus1 - Sus2) + ... + fk (Sorg - Susk) + ... + fN (Sorg - SusN)} where Sproc means the image signal whose dynamic range has been compressed, Sorg is the original image signal, Susk (k = 1 to N) means the unsharp mask image signal, fk (k = 1 to N) means the function of converting the band-limited image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. Apparatus according to claim 19, 20 or 23, wherein forming the bandlimited image signals, forming the converted image signals, forming the integration signal, forming the difference signal, forming the signal with respect to the low frequency components, and adding the signal to the low frequency components to the original image signal using the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sus1, Sus2, ..., SusN)) Fdrc (Sus1, Sus2, ..., SusN) = {f2 (Sus1-Sus2) + f3 (Sus2-Sus3) +. .. + fk (Susk-1-Susk) + ... + fN (SusN-1-SusN)} where Sproc means the image signal whose dynamic range has been compressed, Sorg is the original image signal, Susk (k = 1 to N) means the unsharp mask image signal, fk (k = 2 to N) means the function of converting the bandlimited image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. An apparatus according to claim 19 or 20, wherein the formation of the band-limited image signals, the formation of the converted image signals, the formation of the integration signal, the formation of the difference signal, the formation of the signal with respect to the low-frequency components and the addition of the Si signal with respect to the low-frequency components on the original image signal is carried out with the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sus1, Sus2, ..., SusN)) Fdrc (Sus1, Sus2, ..., SusN) = (1 / N) · {f2 (Sus1-Sus2) + f3 ( Sus1-Sus3) + ... + fk (Sus1-Susk) + ... + fN (Sus1-SusN)} where Sproc means the image signal whose dynamic range has been compressed, Sorg is the original image signal, Susk (k = 1 to N) means the unsharp mask image signal, fk (k = 2 to N) means the function of converting the bandlimited image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor that determines in accordance with the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. An apparatus according to claim 19, wherein said conversion means comprises: limited-picture signal generating means for converting a band-limited picture signal such that the absolute value of the signal value of the band-limited picture signal can be converted into a value not larger than the absolute value, and as appropriate of the absolute value to thereby obtain a limited image signal, an auxiliary image signal generating means for forming an auxiliary image signal which receives a signal of Fre quency band which is below the frequency band of the band-limited image signal used to form the limited image signal, wherein the auxiliary image signal from the original image signal and the plurality of unsharp mask image signals is formed, a zoom ratio signal generating means for converting the auxiliary image signal such that the absolute value of the image signal value of the auxiliary image signal becomes small, the absolute value can be converted into a value near 1, and such that when the absolute value of the signal value of the auxiliary image signal becomes large, the absolute value is converted into a value near 0 to thereby obtain a magnification ratio signal corresponding to the limited image signal, and a multiplier for multiplying the limited image signal by the enlargement ratio signal corresponding to the limited image signal. An apparatus according to claim 19 or 30, wherein the formation of the band-limited image signal comprises forming the band-limited image signals, forming the converted image signals, forming the integration signal, forming the difference signal, forming the signal with respect to the low-frequency components, and adding of the signal relating to the low-frequency components to the original image signal are carried out with the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg-Sus1) * g (Sus1 - Sus2) + f2 (Sus1-Sus2) * g (Sus2-Sus3) + ... + fk (Susk-1-Susk) * g (Susk-Susk + 1) + ... + fN (SusN-1-SusN ) · G (SusN - SusN + 1)} where Sproc means the image signal whose dynamic range has been compressed unless the original image signal is, Susk (k = 1 to N + 1) means the unsharp image signal, fk (k = 1 to N), the function of converting the band-limited image signal to the gain means the limited image signal, g means the function for converting the auxiliary image signal and obtaining the enlargement ratio signal, and D (Sorg-Fdrc) means the dynamic range compression factor determined according to the low-frequency component signal, where D is the function for converting Sorg-Fdrc means. An apparatus according to claim 19 or 30, wherein the formation of the band-limited image signal comprises forming the band-limited image signals, forming the converted image signals, forming the integration signal, forming the difference signal, forming the signal with respect to the low-frequency components, and adding of the signal relating to the low-frequency components to the original image signal are carried out with the following formula: Sproc = Sorg + D (Sorg-Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = {f1 (Sorg-Sus1) * g (Sorg - Sus Sus2) + f2 (Sus1 - Sus2) · g (Sorg - Sus3) + ... + fk (Susk - 1 - Susk) · g (Sorg - Susk + 1) + ... + fN (SusN - 1 - SusN ) · G (Sorg - SusN + 1)} where Sproc means the image signal whose dynamic range has been compressed unless the original image signal is, Susk (k = 1 to N + 1) means the unsharp image signal, fk (k = 1 to N), the function of converting the band-limited image signal to the gain means the limited image signal, g means the function for converting the auxiliary image signal and obtaining the enlargement ratio signal, and D (Sorg-Fdrc) means the dynamic range compression factor determined according to the low-frequency component signal, where D is the function for converting Sorg-Fdrc means. An apparatus according to claim 19, wherein said converting means comprises: auxiliary image signal generating means for converting a low-frequency side band-limited image signal whose frequency band is below the frequency band of the band-limited image signal to be converted with a non-linear function so as to form an auxiliary image signal for said band-image signal To obtain the band-limited image signal to be converted, the nonlinear function having the characteristic that the function can pass through an origin and the slope of the function at the origin has approximately the value 0, and also the slope of the function can gradually increase when the function to be processed Becomes large, a composite band-limited image signal generating means for adding the auxiliary image to the band-limited image signal to be converted, thereby obtaining a composite band-limited image signal, and conversion image signal generating means for converting the composite band-limited image signal so that the absolute value of the signal value of the composite band-limited image signal can be converted into a value not larger than that Is absolute value and determined in accordance with the absolute value, thereby obtaining a converted image signal. Apparatus according to claim 19 or 33, wherein the formation of the bandlimited image signal comprises forming the bandlimited image signals, forming the converted image signals, forming the integration signal, forming the difference signal, forming the signal with respect to the low frequency components, and adding of the signal relating to the low-frequency components to the original image signal are carried out with the following formula: Sproc = Sorg + D (Sorg - Fdrc (Sorg, Sus1, Sus2, ..., SusN)) Fdrc (Sorg, Sus1, Sus2, ..., SusN) = [f1 {(Sorg - Sus1) + g (Sus1 - Sus2)} + f2 {(Sus1 - Sus2) + g (Sus2 - Sus3)} + ... + fk {(Susk - 1 - Susk) + g (Susk - Susk + 1)} + ... + fN {(SusN-1-SusN) + g (SusN-SusN + 1)}] where Sproc is the image signal whose dynamic range has been compressed, Sorg is the original image signal, Susk (k = 1 to N + 1) means the unsharp mask image signal, fk (k = 1 to N) means the composite band-limited image signal converting function , g represents the function for converting the band-limited image signal and obtaining the auxiliary image signal, and D (Sorg-Fdrc) represents the dynamic range compression factor determined according to the low-frequency component signal, where D represents the function for converting Sorg-Fdrc. Device according to one of claims 19, 20, 23 and 26 to 34, wherein the converting means is the plurality of converted image signals generated by that a band-limited image signal corresponding to that part of a Transforms an object whose image was recorded as an original image. Device according to one of claims 19, 20, 23 and 35, at receiving the original image from a tomographic image record was and a flow pattern contains wherein the unsharp mask image signal generator the multiple unsharp mask image signals generated by that a blur mask processing in terms of of the original image signal using a one-dimensional unsharp mask performs, which are in the direction of the flow pattern extends.