Abstract:
Image processing method comprises providing an original image as a matrix of discreet picture elements, splitting the original image into n frequency channels, each channel being presented by an image matrix of the same size as the original image, detecting edges, and assembling an output (enhanced) image from the n frequency channels, the assembling taking the detected edges into account. The n frequency channels are represented by a low frequency channel and n−1 high frequency channels while splitting the original image into frequency channels, and the edge detection is performed by calculating a correlation value between processed pixel and its neighboring pixels in each of n−1 selected high channels followed by comparing the correlation value with that for the corresponding pixels in other high frequency channels and with the threshold value for this channel. Based on the results of the comparison, weighting coefficients are formed for each pixel of each of the n−1 high frequency channels, and the assembling of the output image is made by summing each pixel from the low frequency channel with all products of the corresponding (by their location in the image) pixels of n−1 high frequency channels by their weighting coefficients. The method enhances image sharpness and contrast in conjunction with simultaneous noise suppression.

Description:
TECHNICAL FIELD 
   The present invention relates to the automatics and computation techniques and, more particularly, to methods of the preliminary image processing for sharpening and contrast enhancement. 
   DESCRIPTION OF THE RELATED ART 
   One of the methods known in the prior art is described in the Russian Patent No. 2015561, published on 16 Apr. 1991, Int. Class G06K 9/40. According to this invention, the image correction is made basing on the analysis of the original image at the processed pixel and the local average value over some neighborhood of this pixel. 
   The method of that patent smoothes the original image, thus producing the smoothed image containing the low frequency components presenting the image background. Then the smoothed image is subtracted from the original one producing the second image containing high frequency components without background, said second image is then emphasized and added to the smoothed image. 
   The disadvantage of this method is that it emphasizes not only the useful signal but also the noise containing in high frequency image components, thus degrading the quality of the enhanced image. 
   The method according to U.S. Pat. No. 5,038,388, published on 6 Aug. 1991, Int. Class G06K 9/40, smoothes the original image and subtracts the smoothed image from the original one thus producing the second image containing the high frequency image components only. The second image is then adaptively emphasized so that the larger is a difference between the processed pixels and its neighborhood, the higher are their scaling factors. The output image is produced by adding the adaptively emphasized second image to the original image, thus sharpening the image without the noise amplification. 
   The solution disclosed in this patent does not provide any noise suppression as this method can only emphasize the high frequency image components that may contain a noise. 
   Furthermore, the disadvantage of this method known in the art is that it fails to improve considerably the sharpness of weak edges as such enhancement requires to emphasize the high frequency image components in the regions where a difference between the processed pixel and its neighborhood is comparable to the noise level. Therefore, the edge enhancement in such regions causes the noise emphasis. 
   According to the image processing method disclosed in U.S. Pat. No. 5,381,490, published on 10 Jan. 1995, Int. Class G06K 9/40, the largest difference Δ between the processed pixel and its nearest neighbors is calculated. Depending on the magnitude of this difference, one of the three processing modes is selected:
         edge enhancement by means of emphasis of the high frequency image components if Δ&gt;T 1 , T 1  presenting the first pre-defined threshold value;   reproduction of the original image, if T 2 &lt;Δ&lt;T 1 , where T 2  stands for the second pre-defined threshold value;   image smoothing to suppress a noise, if Δ&lt;T 2 .       

   The first disadvantage of this method is that it may emphasize the noise selectively if the difference Δ varies around any of the threshold values for some neighboring pixels thus transforming small differences between neighboring pixels into larger ones by involving different processing modes for these neighboring pixels. 
   Furthermore, this method fails to provide quality enhancement of images with different noise magnitudes without tuning as the threshold values T 1  and T 2  are not selected adaptively. 
   Another approach to the noise suppression in images is described in U.S. Pat. No. 5,563,963, published on 8 Oct. 1996, Int. Class G06K 9/40. The method of this patent operates by selecting a plurality of groups of neighbors for each pixel of the original image, each group being a square containing N×N pixels, N varying for example from 2 to 21. A processed pixel may be located at any position inside this square group of pixels. The least square best fit plane (the planar approximation) is then used to approximate pixel values in each of selected pixel groups and the new value for the processed pixel and the goodness of fit are computed based on the approximation for each group. 
   The target pixel of an enhanced image is produced by the weighted summing of all the new pixel values, thereat the higher is the goodness of fit for a group the higher is the weight of this group. 
   The disadvantage of this method is that it fails to enhance edges as this method provides the noise smoothing only. Furthermore, this method requires substantial computation efforts to build least square approximations by hundreds of groups for each of hundreds of thousands of pixels. 
   The method disclosed in U.S. Pat. No. 5,739,922, published on 14 Apr. 1998, Int. Classes G06K 9/40, H04N 1/40, operates by splitting an original color image into three isotropic frequency channels: low frequency image components (LF), medium frequency components (MF) and high frequency components (HF). Adaptive emphasis of the HF components and adaptive suppression of the MF components is then carried out, thereat the higher is the correlation between at least two of three basic image colors the higher are multipliers for HF and MF image components. The enhanced image is obtained by summing the LF image components with the adaptively suppressed MF components and the adaptively emphasized HF image components. 
   The image processing method and apparatus described in that patent may have limited application as they are suitable for color images only since it is the correlation between color components only that is used for carrying out the image processing. 
   Furthermore, the noise suppression according to this invention is significantly limited, as the HF image components, that also contain noise, may be emphasized only, and the noise suppression in MF image components is limited because no directional splitting of the original image is used. 
   No edge detection and enhancement can be obtained by this method as the isotropic frequency channels are used. 
   All these disadvantages degrade the quality of enhanced images. 
   The most relevant image processing method is described in U.S. Pat. No. 5,351,305, published on 27 Sep. 1994, Int. Class G06K 9/40. According to this patent, a plurality of directionally filtered images is obtained from the original image by applying directional filters in a frequency domain. An enhanced image is then formed by selecting each target pixel either from a directionally filtered image, if a contrast edge is detected nearby the processed pixel or from the original image otherwise. Thereat, the contrast edge is detected nearby the processed pixel by generating the standard deviation image and by producing an eigenvector description of this image. The eigenvector length is compared to a pre-determined threshold value to detect the edge. 
   The target pixel is equal to the corresponding pixel of the original image, if the edge was not detected nearby. Otherwise, the target pixel is selected from the image filtered with the most nearly corresponding direction of filtering. 
   While detecting edges, the eigenvector length may vary around the threshold value for several adjacent pixels. Whereby, the neighboring pixels of the enhanced image are selected from different images (the original image and directionally filtered image) thus causing the selective noise emphasis. This emphasis degrades the enhanced image quality. 
   Furthermore, original images may differ in their noise levels thus requiring different threshold values. The method does not include adaptive selection of the threshold value and therefore may not provide high quality processing of images with different noise levels. 
   Provided that the edge is detected nearby, the selection of pixels of the enhanced image is made from one of the plurality of directionally filtered images thus causing the complete suppression of all image structures that differ by their direction from the detected edge, notwithstanding that those structures can be clearly seen in the original image. 
   SUMMARY OF THE INVENTION 
   The object of the claimed invention is to provide an improved method for enhancing the image sharpness and contrast combined with simultaneous noise suppression. 
   This objective according to the present invention is achieved in a image processing method, comprising the steps of providing an original image as a matrix of discreet picture elements (pixels), splitting the original image into n frequency channels, each of the channel being presented by an image matrix of the same size as the original image, detecting edges and assembling the output image from said n frequency channels taking the detected edges into account. 
   The step of splitting the original image is performed into a low frequency channel and n−1 high frequency channels and the edge detection is performed by computation in each of n−1 selected high frequency channels of a correlation between processed pixel and its neighboring pixels, followed by a comparison of the correlation value with that for corresponding pixels in other high frequency channels and with a threshold value for this channel. Based on the results of the comparison, weighting coefficients are formed for each pixel of each of the n−1 high frequency channels, and the assembly of the output image is made by summing each pixel from the low frequency channel with all products of the corresponding (by their location in the image) pixels of the n−1 high frequency channels by their weighting coefficients. 
   The objectives of the invention is also achieved by selection of m of the n−1 high frequency channels (2&lt;m&lt;=n−1) in such a way that they differ one from another in the direction of principal passing only. Therewithal, the weighting coefficients for any of the pixels of any of the m high frequency channels are defined based on the comparison of its correlation value to the threshold value and to the correlation values of the corresponding (by their location in the image) pixels of other m−1 high frequency channels. 
   The objectives of the invention is also achieved by computation of correlation values as a product of the processed pixel value and the weighted sum of its neighbors, the weights being anisotropic, the direction of this anistropy corresponding to the direction of principal passing of the processed channel. 
   Furthermore, the threshold value for any of the n−1 high frequency channels is determined by analysis of distribution of values, or absolute values, of pixels in this channel. The threshold value may be also determined by analysis of distribution of values, or absolute values, of pixels of the original image. 
   The image processing method claimed as the invention is characterized by the following features that distinguish it from the most relevant method known in the prior art: 
   1. while splitting the original image into n frequency channels, the low frequency channel and n−1 high frequency channels are selected; 
   2. the value of correlation between the processed pixel and its neighboring pixels is used to detect edges in the image. More specifically, the value of the correlation is compared to the correlation values for the corresponding (by their location in the image) pixels in other n−1 high frequency channels and to the threshold value for this channel. The weighting coefficients for all pixels of the n−1 high frequency channels are generated as a result of this comparison; 
   3. the output image is assembled by means of summing each pixel from the low frequency channel and all the corresponding (by their location) pixels from the n−1 high frequency channels multiplied by their respective weighting coefficients. 
   Concerning the first feature, it should be noted that the extraction of the low frequency channel that is not a subject of any processing provides the distortion-free reproduction of large-scale structures of the original image in the enhanced image, as these objects are passed through the low frequency channel without any processing. Therewithal the energy of noise passing through the low frequency channel without suppression is inherently low as most of the noise energy is concentrated at high frequencies. 
   Furthermore, the extraction of the low frequency channel helps reduce the computation power needed to find correlation values for n−1 high frequency channels, as the subtraction of the local average value is required to compute correlation. This subtraction is made while extracting (subtracting from the original image) the low frequency channel. 
   The splitting of the original image into several high frequency channels helps improve, as compared to selection of one or two channels only, the noise suppression, as the noise associated with pixels of those frequency channels, where edges were not found nearby, is prevented from contributing to the enhanced image. 
   Concerning the second feature, it should be noted that edge detection by means of the correlation value between the processed pixel and its neighbors allows to find weak edges against a noisy background as the correlation value is close to zero for a typical noise, thereat the correlation values for adjacent pixels forming the edge are positive and of a similar magnitude. This difference in a correlation behavior helps achieve high noise suppression combined with edge emphasis thus increasing the image quality. 
   The edge detection based on the correlation between a processed pixel and its neighbors makes the method of the present invention applicable to various types of images, including color and gray scale and vector images, as well as multi-dimensional images. Furthermore, the use of this correlation provides a quantitative measure of the edge intensity for each image pixel. This feature allows performing the selective emphasis of edges having certain intensity, for example, weak edges. 
   The third specific feature, namely assembling the enhanced image by weighted summing images from all frequency channels, helps remove completely the effect of a selective noise emphasis. This effect is caused by selection of neighboring pixels from different frequency channels. 
   Furthermore, the determination of the threshold value by means of analysis of statistical distribution of pixels provides a high quality processing of images with materially different noise magnitudes without changing parameters. 
   Furthermore, the use of anisotropic frequency channels and anisotropic weights makes the image processing method highly sensitive to weak edges. 

   
     BRIEF DESCRIPTION OF DRAWINGS 
     These and other features and advantages of the invention will appear from the following description in which a preferred embodiment has been set forth in detail in conjunction with the accompanying drawings of the apparatus that implements the claimed method, where 
       FIG. 1  is a block diagram, of the apparatus. 
     The preferred embodiment of the sub-units of said apparatus are shown in more detail in  FIGS. 2–5 , where 
       FIG. 2  is a block diagram of a frequency channel splitting unit  2  of  FIG. 1  (further referred to as splitting unit); 
       FIG. 3  is a block diagram of one channel of a unit  3  of  FIG. 1  for computation of correlations (further referred to as correlation unit); 
       FIG. 4  is a block diagram of a unit  4  of  FIG. 1  for forming weighting coefficients (further referred to as weighting unit); 
       FIG. 5  is a block diagram of the unit  5  of  FIG. 1  to assemble an output image (further referred to as assembling unit); 
       FIG. 6  shows an example of pre-defined channel selection matrixes; 
       FIG. 7  illustrates the operation of the splitting unit; and 
       FIG. 8  is a graph showing an example of the dependence of the weighting coefficient on the correlation value. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   Referring to  FIG. 1 , the apparatus contains an image source  1 , an output of the image source being connected to an input of a splitting unit  2 . A low frequency output  7  of the splitting unit  2  is connected to an input  9  of an assembling unit  5 , wherein all other outputs of the splitting unit  2  are connected to corresponding inputs of a correlation unit  3 . These other outputs are also connected to inputs  10   1 – 10   4  of the assembling unit  5 . Outputs of the correlation unit  3  are connected to the corresponding inputs of a weighting unit  4 , its outputs being inputs  8   1 – 8   4  of the assembling unit  5 . Thereat an output of the assembling unit  5  is connected to an input of a memory unit  6 , an output of the memory unit  6  being an output of the apparatus. 
     FIG. 2  shows the preferred embodiment of the splitting unit  2  in more detail. The unit includes a direct Fourier processor  11 , whose input processor is connected to the output of image source  1 , whereas an output of the direct Fourier processor  11  is connected to first inputs of matrix multiplicators  12   0 – 12   4 . Second inputs of the multiplicators are connected to corresponding memory units  13   0 – 13   4 , the memory units holding pre-defined channel selection matrixes shown in  FIG. 6 . Any of the matrix multiplicators  12   0 – 12   4  performs an element-by-element multiplication of matrixes supplied to its two inputs. Outputs of the matrix multiplicators  12   0 – 12   4  are connected to inputs of inverse Fourier processors  14   0 – 14   4 . An output of the inverse Fourier processor  14   0  is connected to the input  9  of the assembling unit  5 , and outputs of the inverse Fourier processors  14   1 – 14   4  are connected to the inputs  10   1 – 10   4  of the assembling unit  5 , as well as to the corresponding inputs of the correlation unit  3 . 
     FIG. 3  shows in more detail one channel of the correlation unit  3 . A memory unit  15  holds the image of the respective frequency channel. An input of this memory unit is the input of the correlation unit  3 . It is connected also to input of a noise level measuring unit  20 , an output of which unit is connected to a first input of a divider  19 . A second input of the divider  19  is connected to an output of a multiplicator  18  whose first input is connected to a first output of the memory unit  15 . Other outputs of the memory unit  15  are connected to a weighting adder  17 . An output of the weighting adder is a second input of the multiplicator  18 . An address input of the memory unit  15  is connected to an address generator  16 . An output of the divider  19  is the output of the correlation unit  3 . 
   The noise level measuring unit  20  may be implemented according to the U.S. Pat. No. 5,657,401, published on 12 Aug. 1997, Int. Class G06K 9/40, which is incorporated herein by reference. 
   All of the memory units are of the random access memory type, and DIMM PC133 128 Mb memory modules manufactured by IBM, or other similar ones well known in the art can be used for that purpose. 
   The weighting adder  17  may be implemented as eight scalar multiplicators (the number of the multiplicators is equal to the number of adjacent pixels) and an adder. Any of the scalar multiplicators has two functionally identical inputs and one output. The outputs of all of the scalar multiplicators are connected to inputs of the adder, the output of the adder being the output of the unit  17 . The first inputs of the scalar multiplicators are the inputs of the unit  17 , and pre-defined weighting coefficients are supplied to the second inputs of the multiplicators. 
     FIG. 4  shows the weighting unit  4 . The four inputs of the weighting unit  4  are inputs of four rounding units  23   1 – 23   4 , outputs of the rounding units are connected to inputs of an address assembling unit  24 . An output of the unit  24  is connected to inputs of four look-up tables  25   1 – 25   4 . The look-up table is a memory unit that stores the values of a weighting coefficient for any set of the four input correlation values. Outputs of the look-up tables  25   1 – 25   4  are connected to inputs of memory units  26   1 – 26   4 , accumulating values of the weighting coefficients. Address inputs of the memory units  26   1 – 26   4  are connected to an address generator  27 , and outputs of the memory units  26   1 – 26   4  are connected to inputs of adders  28   1 – 28   4  for averaging the weighting coefficients. Outputs of the adders are the outputs of the weighting unit  4 . 
     FIG. 5  shows the assembling unit  5 . It consists of four multiplicators  29   1 – 29   4  and an adder  30 . The first inputs  8   1 – 8   4  of the multiplicators are connected to the outputs of the weighting unit  4 , and the second inputs  10   1 – 10   4  of the multiplicators are connected to the outputs of the splitting unit  2 . The outputs of the multiplicators  29   1 – 29   4  are connected to corresponding inputs of the adder  30 , and, as was disclosed in the above, the input  9  of the adder is connected to the low frequency output  7  of the splitting unit  2 . An output of the adder  30  is the output of the assembling unit  5 , and it is connected to the input of the memory unit  6  that accumulates an enhanced image. 
   The apparatus implements the claimed method as it is described hereinafter in more detail. Referring to  FIG. 1 , an input image is generated by the image source  1 . A Magnetic Resonance Imaging (MRI) unit may be used, for example, as the image source  1 . This MRI unit produces an image of a cross-section of an object, this image being a matrix containing discreet picture elements (pixels). The image is carried to the input of the splitting unit  2 . The operation of the splitting unit  2  is described with a reference to  FIG. 2 ,  FIG. 6 , and  FIG. 7 . The input image is transformed to the frequency presentation by the direct Fourier processor  11 . This frequency presentation contains complete information about the original image and is represented by the matrix of the same size as an input image. This matrix is passed to identical matrix multiplicators  12   0 – 12   4 , performing element-by-element multiplication of the frequency presentation of the original image by pre-defined channel selection matrixes. The channel selection matrixes are stored in the memory units  13   0 – 13   4 . Each channel selection matrix contains multipliers for all spatial frequencies of the frequency image presentation.  FIG. 6  shows examples of the channel selection matrixes. 
   More specifically, as the image is presented by a 2D matrix, its frequency presentation is also a 2D matrix.  FIG. 6   a  shows schematically a frequency presentation matrix. The horizontal and vertical spatial frequencies vary along axes k x , and k y , respectively. 
   The zero spatial frequency corresponds to the constant image density. It is located at the crossing point ( 31 ) of the axes k x  and k y . 
   Points  32  and  33  represent the largest spatial frequency in the horizontal direction. The examples of images contributing to these points are shown in drawings  34  and  35 . 
   Similarly, the maximal spatial frequency in the vertical direction is located at points  36  and  37 ; the example of the image contributing to these points is illustrated by drawing  38 . 
   The maximal spatial frequencies are located at points  39 – 42 . The example of the image contributing to these maximal spatial frequencies is shown in drawing  43 . 
   A medium spatial frequency in the horizontal direction is located at point  45 . The example of the image contributing to this point is shown in drawing  44 . 
   The location of the spatial frequencies in drawings  FIG. 6(   b–f ) corresponds to the scheme depicted in  FIG. 6   a.    
     FIG. 6   b  shows schematically the pre-defined selection matrix for the low frequency channel, this matrix being stored in the memory unit  13   0 . 
   The dark area  46  is filled by the unit values of the matrix elements. This area corresponds to spatial frequencies that pass through the low frequency channel. The white region is filled by the zero values of the matrix elements, therefore the frequencies of the white region do not pass through the low frequency channel. 
     FIG. 6(   c–f ) show schematically the selection matrixes for four high frequency channels, the same notations as in  FIG. 6   b  being used thereat. 
   It should be noted that the sum of all channel selection matrixes in  FIG. 6(   b–f ) is the matrix with all elements equal to 1. Therefore, all the information from the original image passes through at least one channel. 
   Referring now to  FIG. 2 , each of the matrix multiplicators  12   0 – 12   4  forms on its output the matrix of the corresponding frequency channel in the frequency presentation. The inverse Fourier processors  14   0 – 14   4  transform these matrixes to the coordinate presentation. 
   The direct Fourier processor  11  and inverse Fourier processors  14   0 – 14   4  may be implemented based on the Fast Fourier Transform algorithm, as described, for example, in: Cooley, J. M., Lewis, P. A. W. and Welch, P. D.  The Finite Fourier Transform  I.E.E.E. Trans. Audio Electroacoustics AU-17, 2, 77–86 1969. 
     FIG. 7  further illustrates operation of the splitting unit.  FIG. 7   a  shows the example of an input image,  FIGS. 7(   b–f ) show the images formed on the outputs of the inverse Fourier processors  14   0 – 14   4 , respectively, as a result of processing the image shown in  FIG. 7   a . The image of a low frequency channel  7   b  is carried from the output of the Fourier processor  14   0 , being the output  7  of the splitting unit  2  to the input  9  of the assembly unit  5 . The images of four high frequency channels are carried from the outputs of the Fourier processors  14   1 – 14   4 , being other outputs of the splitting unit  2  to the corresponding inputs of the correlation unit  3  and to the inputs  10   1 – 10   4  of the assembly unit  5 . 
   The further processing of these images will be described by the example of the first high frequency channel as this processing is identical in all high frequency channels. 
   Referring to  FIG. 3 , the memory unit  15  stores the partial image of the processed channel. To compute the unnormalized correlation value, the processed pixel value  21  and values of its neighboring pixels  22  are sequentially selected from the memory  15 . These values of neighboring pixels pass to the input of weighting adder  17 . The adder  17  implements the following operation on pixel values: 
       r   =       ∑     i   =   1     N     ⁢       V   i     ⁢     X   i             
 
where N is a number of pixels in neighborhood  22  of the processed pixel (preferably N=8), V i  are the pre-defined weighting coefficients (preferably V i =⅛) and X i  are the pixel values from neighborhood  22 .
 
   Alternatively, anisotropic weights V i  can be used for computation of the weighted sum r of the neighboring pixels. This approach is beneficial for cases where several high frequency channels differ one from another by the direction of their principal passing only. The direction of anisotropy of the weights V i  corresponds to the direction of principal passing for the processed frequency channel. 
   The multiplicator  18  forms a product of the weighted sum of neighboring pixels and the processed pixel value. This product is the unnormalized correlation value for the processed pixel. It is compared to the threshold value by dividing by this threshold value (output of the noise level measuring unit  20 ) in the divider  19 . The result of this division is compared to 1.0 in the weighting unit  4 . The processing described herein is repeated for all of the pixels of the partial image of the processed frequency channel. 
   The image of this first frequency channel passes also to the noise level measuring unit  20 . The noise level from the output of the unit  20  is used as a threshold value to normalize correlation values by the divider  19 . As a result, the matrix containing the correlation values for all of the pixels of the processed frequency channel is formed on the output of the correlation unit  3 , these correlation values being normalized by the threshold value for the processed frequency channel. 
   The correlation values formed by the correlation unit  3  are carried to the weighting unit  4 . Referencing to  FIG. 4 , those correlation values for four frequency channels pass to inputs of the rounding units  23   1 – 23   4 . The rounding units decrease the data precision to 4 or 5 bits. 
   The four rounded values from outputs of the rounding units  23   1 – 23   4 , each containing 4 or 5 bits, are assembled into one 16- or 20-bit word by the address assembling unit  24 . The address formed thereby is used as an input value for the four look-up tables  25   1 – 25   4 . Each of them is a memory unit that stores values of the weighting coefficients for any combination of four correlation values in four frequency channels, such combination defining thereat the address formed by the unit  24  in a unique way. 
   The comparison of the correlation value with correlation values for the corresponding pixels in other high frequency channels is performed by the look-up tables  25   1 – 25   4 . The values of weighting coefficients stored in these look up tables depend on the correlation values from all 4 channels. 
     FIG. 8  shows the preferred dependence of a weighting coefficient W i  in any of the frequency channels on the correlation value C i  in this channel and correlation values in other three channels, Δ representing the threshold value for tills frequency channel. The weight Wi depends on the correlation value Ci and maximal correlation value L in other three frequency channels. This dependence is illustrated in  FIG. 8  by the plurality of curves:
         curve A for C i ≧0.7 L,   curve B for C i =0.5 L,   curve C for C i =0.3 L,   curve D for C i =0.1 L, and   curve E for C i =0.01 L.       
   The weighting coefficient W i  takes a minimal value for correlation values that are significantly smaller than the threshold value Δ. This part of dependencies shown in  FIG. 8  provides the noise suppression. The weighting coefficient smoothly increases from its minimal value to its maximal value for correlation values that are close to the threshold value Δ. Finally, the weighting coefficient takes its maximal value for correlation values that are significantly larger than Δ. This part of dependencies illustrated by  FIG. 8  provides non-distorted reproduction of edges. 
   Alternatively, the weighting coefficient W i  may increases from its minimal value to its maximal value while the correlation value is in the range between the threshold value Δ and the second threshold value, being equal to the first threshold value Δ multiplied by some constant coefficient. The weighting coefficient W i  decreases when the correlation value becomes higher than the second threshold value. Therefore the weighting coefficient W i  takes a maximum for correlation values larger than the threshold value Δ. This behavior of the weighting coefficient W i  provides the emphasis of the weak edges thus improving the image quality. 
   The memory units  26   1 – 26   4  accumulate values of the weighting coefficients generated by look-up tables  25   1 – 25   4 . The address generator  27  and adders  28   1 – 28   4  smooth the weighting coefficients in each frequency channel. The smoothing is obtained by summing in the adder (for example,  28   1 ) the center value of the coefficient and its neighboring values being sequentially selected from the memory unit (for example,  26   1 ) by the address generator  27 . The smoothed values of the weighting coefficients formed on the outputs of the adders (for example,  28   1 ) pass to the outputs of the weighting unit  4 . 
   The operation of the assembling unit  5  is described with a reference to  FIG. 5 . The values of weighting coefficients for four frequency channels pass from the outputs of the unit  4  to the inputs  8   1 – 8   4  of multiplicators  29   1 – 29   4 , whereas the pixel values of the corresponding frequency channels are carried from outputs of the splitting unit  2  to other inputs  10   1 – 10   4  of these multiplicators. The products of the pixel values by the corresponding weighting coefficients generated by multiplicators  29   1 – 29   4  pass to the inputs of the adder  30 . Thereto the corresponding pixel value of the low frequency channel passes to the input  9  of the adder. The adder  30  adds the pixel value of the low frequency channel to all values of the corresponding (by their location in the image) pixels of the high frequency channels, the latest values are multiplied (in multiplicators  29   1 – 29   4 ) by their respective weighting coefficients. The memory unit  6  accumulates pixels of the output image. 
   The embodiment described herein illustrates the method as applied to 2D scalar images. It is understood however that the claimed method may be applied similarly to 3D images. In this case, in the apparatus used to implement the claimed method, the number of frequency channel increases, the 3D Fourier processors are used instead of 2D ones, and the number of pixels in the neighborhood of the processed pixel (used, for example, to compute a correlation value) is 26 instead of 8. 
   The claimed method may be applied also to processing vector images, particularly the color images. Thereat, three components of a vector presenting a pixel value may correspond, for example, to the intensity of the three basic colors for this pixel. In this case, the scalar operations on pixel values, like Fourier transform and summing, are replaced by the corresponding vector operations as it is known in the art, and the correlation is computed as a scalar product of the center pixel value and the weighted vector sum of its neighbors, thereat the vector adder contains as many scalar adders as the number of vector components. 
   For different parts of the image, different threshold values can be used to form weighting coefficients in at least one of the n−1 high frequency channels. When a picture element is represented by a scalar value, the threshold values for the different parts of the image and different high frequency channels are determined by analyzing distribution of pixel values in a corresponding part of the image of a corresponding frequency channel. On the other hand, when a picture element is represented by a vector, the threshold values for the different parts of the image and different high frequency channels are determined by analyzing distribution of absolute values of vectors representing pixels in a corresponding part of the image of a corresponding frequency channel. 
   INDUSTRIAL APPLICABILITY 
   The image processing method according to the invention has the following advantages: 
   First, the use of the correlation between the processed pixel and its neighboring pixels helps detect weak edges against a noisy background. This feature provides high noise suppression in conjunction with the emphasis of weak edges, thus significantly improving the image quality. Furthermore, use of correlation between the processed pixel and its neighbors makes the method applicable to a wide variety of image types, including color images, gray scale images and 3D images. 
   Second, the determination of the threshold value by analysis of distribution of pixel values provides a high quality processing of images with the material difference in their noise levels without changing parameters. 
   Third, the distortion-free reproduction of large-scale image structures is achieved due to separation of the low frequency channel.