Abstract:
The basic configuration of Single local Adaptive Window Spatial Noise Reducer (SAW-SNR) is based on a preliminary de-noising low-pass filter followed by homogenous region segmentation to the considered pixel in a given local window. The configuration is composed also of an adaptive local mean estimator, an adaptive local statistic estimator which is preferably an economic standard deviation (SD) estimator and finally, a minimum-mean-square-error (MMSE) based de-noising technique. The proposed segmentation configuration outperforms existing spatial noise reducers in term of subjective and objective performances, in term of edge preservation, noise reduction in both homogenous regions or picture edges and Peak Signal to noise Ratio (PSNR). A second configuration in the form of a Parallel Multiple local Adaptive Window Spatial Noise Reducer (Parallel M-AW-SNR), is a combination of several basic configurations which implements different segmented windows. The M-AW-SNR, which is the less complex configuration for multiple spatial noise reducers, reduces further residual noise as compared to the basic configuration. A third configuration combines the basic configuration of SAW-SNR with a controllable noise variance estimator. This generic configuration allows an adaptive local control of noise reduction level, which can be useful for some correlated noise such as ringing noise in DCT-based decompressed images or cross-luminance noise in composite decoded images.

Description:
FIELD OF THE INVENTION 
     The present invention relates to the field of image noise reducing, and more particularly to apparatus and method for adaptively reducing noise in a noisy input image or a sequence of images, wherein the noise is of an additive Gaussian type. 
     DESCRIPTION OF THE PRIOR ART 
     While existing prior patents and other publications on noise reducing techniques are abundant, they generally concern coring techniques applied in high frequency part of a considered image. Image de-noising techniques can be classified as spatial or temporal ones. Of course, a series combination of spatial and temporal techniques is possible and generally beneficial. 
     Temporal filters are generally applied for a sequence of images in which the noise component between two successive images is supposed to be non-correlated. The temporal filtering techniques are essentially based either on motion detection or motion estimation. The filter structure can be either infinite impulse response (IIR) or finite impulse response (FIR) with frame delay elements. In general, the temporal techniques are more performing than the spatial ones. The system cost is essentially due to the frame memory and the motion estimation hardware. Temporal de-noising techniques can be found, for example, in U.S. Pat. No. 5,161,018 to Matsugana; U.S. Pat. No. 5,191,419 to Wicshermann; U.S. Pat. No. 5,260,775 to Farouda; U.S. Pat. No. 5,404,179 to Hamasaki and in a recent publication entitled “ The Digital Wetgate: A Third-Generation Noise Reducer ”, Wischerman; G., SMPTE Journal, February 1996, pp. 95-100. 
     Spatial noise reducing techniques can be applied for either still pictures or sequence of images. These techniques are described in many available textbooks such as: “ Fundamentals of Electronic Image Processing ”, Weeks; A. R. Jr., SPIE Optical Engineering Press, Bellingham, Wash., 1996; “ Two - Dimensional Signal and Image Processing ”, Lim; J. S., Prentice-Hall, Englewood Cliffs, N.J., 1990; and “ Nonlinear Digital Filters: Principles and Applications ”, Pitas, J. and al., Kluwer Academic Publishers, Boston, 1990. In general, spatial noise reducing techniques can be divided further into three categories. 
     In the first category, the spatial nonlinear filters are based on local order statistics. Utilizing a local window around a considered pixel, these filters are working on this set of pixels ordered from their minimum to their maximum values. For example, the median filter, the min/max filter, the alpha-trimmed mean filter, and their respective variants can be classified in this category. These filters work well for removing impulse like salt-and-pepper noise. However, for the small amplitude noise these filters can blur some details or small edges. 
     In the second category the coring techniques are applied in another domain different from the original image spatial domain. The chosen domain partly depends on noise nature. U.S. Pat. No. 4,163,258 to Ebihara teaches the use of the Walsh-Hadamard transform domain, while U.S. Pat. No. 4,523,230 to Carlson et al. discloses some sub-band decomposition. Finally, the homomorphic filter, working in the logarithmic domain, is the classical one for removing multiplicative noise and shading from an image. 
     In the third category, the filters are locally adaptive and the noise removing capacity is varying from homogenous regions to edge regions. These filters give good results for additive Gaussian noise. A well-known filter in this category is the minimum-mean-square-error (MMSE) filter as originally published in “ Digital image enhancement and noise filtering by use of local statistics ”, Lee; J. S., IEEE Trans. on PAMI-2, March 1980, pp. 165-168. Referring FIG. 1, a general block diagram of the prior art Lee&#39;s MMSE noise reducer is illustrated. Let a fixed dimension window centered on the considered or current pixel. The filtered pixel output f*(x,y) is additively composed of the local mean value obtained at output  12  of a mean estimator  10  and a weighted difference of the noisy pixel g(x,y) and the local mean intensity values. The optimum weight K determined by MMSE at an output  14 , which corresponds to a kind of coring technique, is equal to the local variance ratio of the true image and the noisy one. The Lee&#39;s MMSE filter efficiently removes noise in homogenous image regions while reserving the image edges. However, the noise essentially remains in edge or near-edge regions. Moreover, the required variance calculation is expensive for hardware implementation. 
     In “ One-dimensional processing for adaptive image restoration ”, Chan; P. et al., IEEE Trans. on ASSP-33, February 1985, pp. 117-126, there is presented a method for noise reducing in edge regions. The authors propose the use, in series, of four (4) one-dimensional MMSE filters respectively along 0°, 45°, 90° and 135° directions. The obtained results are impressive for large variance noise. However, for small noise, the filter can blur some image edges. Moreover, the noise variance output estimation at each filter stage require costly hardware. 
     For a same purpose, in “ Digital image smoothing and the Sigma filter ”, Lee; J. S., Computer Vision, Graphics, and Image Processing-24, 1983, pp. 255-269, there is proposed a said Sigma filter as illustrated in FIG.  2 . For noise removing, this filter calculates with a segmentation processor 16 using a local window of 5×5 dimensions combined to a mean estimator 18, the mean value of similar pixel intensities to that of a central considered pixel g(x,y), to obtain f*(x,y). A pixel in the window is said similar to the considered pixel if the intensity difference between these two pixels is smaller than a given threshold value. Usually, the threshold value is set equal to twice the noise standard deviation. For small noise, the Sigma filter works well, except for some pixels with sharp spot noise. For the latter case, J. S. Lee has suggested also, in a heuristic manner, the use of immediate neighbor average at the expense of some eventually blurred picture edges. Generally, the MMSE filters yield better objective results in term of peak signal to noise ratio (PSNR), than the Sigma filter. 
     Independently to the Lee&#39;s contribution, U.S. Pat. No. 4,573,070 to Cooper essentially discloses a Sigma filter for a 3×3 window. Moreover, in the same Patent, Cooper finally combines in a single configuration the said Sigma filter, an order statistic filter and a strong impulse noise reduction filter. 
     In the above-cited publication of A. R. Weeks Jr., there is described the adaptive double-window-modified-trimmed mean (DW-MTM) filter. This filter yields as output the trimmed mean value of similar pixel intensities to the median value in a local window. The adaptive DW-MTM filter is able to eliminate both salt-and-pepper noise and Gaussian noise but at the expense of filtered image blurring. 
     Finally, for correlated noise such as ringing/quantified noise in Discrete-Cosine-Transform (DCT) based decompressed image or cross-luminance noise in NTSC/PAL decoded image, there is still a need for a generic configuration or technique for implementing a real time noise reducer. 
     SUMMARY OF THE INVENTION 
     It is therefore an object of the present invention to provide an apparatus and a method for adaptively reducing noise in a noisy input image signal which provides spatial noise reduction in both homogenous and edge regions using a robust adaptive local segmented window, while preserving picture edges. 
     Another object of an aspect of the present invention is to provide, via a local segmented window, a technique that can adaptively estimate local mean and local standard deviation or variance in a non-stationary environment of a picture. 
     Yet another object of an aspect of the present invention is to provide an economic MMSE-based spatial noise reducer. 
     Yet another object of an aspect of the present invention is to provide a combined spatial noise reducer in which multiple local segmented windows are considered. 
     Yet another object of an aspect of the present invention is to provide a generic configuration for correlated noise variance estimator and for controllable mechanism of local noise reduction level. 
     According to one or more of the above objects, from a broad aspect of the present invention, there is provided an adaptive apparatus for spatially reducing noise in a noisy input image signal comprising a low-pass filter receiving the noisy input image signal to generate a noisy low-spatial frequency image signal. The apparatus further comprises a first pixel-based serial-to-parallel converter receiving the noisy low-spatial frequency image signal to generate a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to predetermined pixels-window characteristics, a pixel-based local window segmentation processor comparing values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with the locally considered pixel value to generate segmented local window parallel signals associated with selected pixels, and a counter generating a selected-pixels count signal. The apparatus further comprises a second pixel-based serial-to-parallel converter receiving the noisy input image signal to generate a group of noisy input image parallel signals associated with the locally considered pixel and according to the predetermined pixels-window characteristics. The apparatus also comprises a mean estimator combining the noisy input image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a mean pixel value signal associated with the locally considered pixel, and a minimum-mean-square-error filter receiving the noisy input image parallel signals, the segmented local window parallel signals, the selected-pixels count signal and the mean pixel value signal to generate a noise-filtered output image signal according to an input noise statistic signal. 
     According to a further broad aspect of the invention, there is provided a an adaptive apparatus for spatially reducing noise in a noisy input image signal comprising a plurality of parallel-connected adaptive spatial noise reducers each presenting a distinct set of predetermined pixels-window characteristics, each said noise reducer receiving the noisy input image signal to generate a corresponding pre-filtered output image signal and an averaging unit receiving each pre-filtered output image signal at a corresponding positive input thereof to generate a noise-filtered output image signal. Each noise reducer comprises a low-pass filter receiving the noisy input image signal to generate a noisy low-spatial frequency image signal and a first pixel-based serial-to-parallel converter receiving the noisy low-spatial frequency image signal to generate a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to the set of predetermined pixels-window characteristics. Each noise reducer further comprises a pixel-based local window segmentation processor comparing values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with the locally considered pixel value to generate segmented local window parallel signals associated with selected pixels, a counter generating a selected-pixels count signal, and a second pixel-based serial-to-parallel converter receiving the noisy input image signal to generate a group of noisy input image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics. Each noise reducer also comprises a mean estimator combining the noisy input image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a mean pixel value signal associated with the locally considered pixel, and a minimum-mean-square-error filter receiving the noisy input image parallel signals, the segmented local window parallel signals, the selected-pixels count signal and the mean pixel value signal to generate the pre-filtered output image signal according to an input noise statistic signal. 
     According to another broad aspect of the invention, there is provided an adaptive apparatus for spatially reducing noise in a noisy input image signal comprising an adaptive spatial noise reducer including a low-pass filter receiving the noisy input image signal to generate a noisy low-spatial frequency image signal, and a first pixel-based serial-to-parallel converter receiving the noisy low-spatial frequency image signal to generate a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to a set of predetermined pixels-window characteristics. The adaptive spatial noise reducer further includes a pixel-based local window segmentation processor comparing values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with the locally considered pixel value to generate segmented local window parallel signals associated with selected pixels, a counter generating a selected-pixels count signal, and a second pixel-based serial-to-parallel converter receiving the noisy input image signal to generate a group of noisy input image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics. The adaptive spatial noise reducer also includes a mean estimator combining the noisy input image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a mean pixel value signal associated with the locally considered pixel, and a minimum-mean-square-error filter receiving the noisy input image parallel signals, the segmented local window parallel signals, the selected-pixels count signal and the mean pixel value signal to generate a noise-filtered output image signal according to an input noise statistic signal. The apparatus further comprises a controllable noise statistic estimator including a high-pass two-dimensional filter receiving the noisy input image signal to generate a noisy horizontal/vertical high-spatial frequency image signal, and a third pixel-based serial-to-parallel converter receiving the noisy horizontal/vertical high-spatial frequency image signal to generate a group of noisy horizontal/vertical high-spatial frequency image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics. The controllable noise statistic estimator further includes a statistic calculator combining the noisy horizontal/vertical high-spatial frequency image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a resulting noise statistic signal associated with the locally considered pixel, and a noise statistic estimator unit generating the input noise statistic signal from the resulting noise statistic signal. 
     According to another broad aspect of the invention, there is provided an adaptive method for spatially reducing noise in a noisy input image signal comprising the steps of: i) filtering the noisy input image signal to generate a noisy low-spatial frequency image signal; ii) converting the noisy low-spatial frequency image signal to a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to a set of predetermined pixels-window characteristics; iii) comparing values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with the locally considered pixel value to generate segmented local window parallel signals associated with selected pixels; iv) generating a selected-pixels count signal; v) converting the noisy input image signal to a group of noisy input image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics; vi) combining the noisy input image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a mean pixel value signal associated with the locally considered pixel; and vii) processing the noisy input image parallel signals with a minimum-mean-square-error filter using the segmented local window parallel signals, the selected-pixels count signal and the mean pixel value signal to generate a noise-filtered output image signal according to an input noise statistic signal. 
     According to another broad aspect of the invention, there is provided an adaptive method for spatially reducing noise in a noisy input image signal comprising the steps of: i) filtering the noisy input image signal to generate a noisy low-spatial frequency image signal; ii) converting the noisy low-spatial frequency image signal to a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to a set of predetermined pixels-window characteristics; iii) comparing values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with the locally considered pixel value to generate segmented local window parallel signals associated with selected pixels; iv) generating a selected-pixels count signal; v) converting the noisy input image signal to a group of noisy input image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics; vi) combining the noisy input image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a mean pixel value signal associated with the locally considered pixel; vii) processing the noisy input image parallel signals with a minimum-mean-square-error filter using the segmented local window parallel signals, the selected-pixels count signal and the mean pixel value signal to generate a pre-filtered output image signal according to an input noise statistic signal; viii) repeating said steps ii) to vii) according to at least one further complementary set of pixels-window characteristics to generate a further pre-filtered output image signal according to the input noise statistic signal; and ix) averaging said output image signals to generate a noise-filtered output image signal. 
     According to another broad aspect of the invention, there is provided an adaptive method for spatially reducing noise in a noisy input image signal comprising: i) filtering the noisy input image signal to generate a noisy low-spatial frequency image signal; ii) converting the noisy low-spatial frequency image signal to a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to a set predetermined pixels-window characteristics; iii) comparing values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with the locally considered pixel value to generate segmented local window parallel signals associated with selected pixels; iv) generating a selected-pixels count signal; v) converting the noisy input image signal to a group of noisy input image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics; vi) combining the noisy input image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a mean pixel value signal associated with the locally considered pixel; vii) filtering the noisy input image signal to generate a noisy horizontal/vertical high-spatial frequency image signal; viii) converting the noisy horizontal/vertical high-spatial frequency image signal to a group of noisy horizontal/vertical high-spatial frequency image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics; ix) combining the noisy horizontal/vertical high-spatial frequency image parallel signals with the segmented local window parallel signals and the selected-pixels count signal to generate a resulting noise statistic signal associated with the locally considered pixel; x) generating an input noise statistic signal from the resulting noise statistic signal; and xi) processing the noisy input image parallel signals with a minimum-mean-square-error filter using the segmented local window parallel signals, the selected-pixels count signal and the mean pixel value signal to generate a noise-filtered output image signal according to the input noise statistic signal. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The preferred embodiments of the present invention will now be described with reference to the accompanying drawings, in which: 
     FIG. 1 is a general block diagram of the prior art Lee&#39;s MMSE noise reducer; 
     FIG. 2 is a general block diagram of the prior art Lee&#39;s Sigma filter; 
     FIG. 3 is a block diagram illustrating a first preferred embodiment of a spatial noise reducer according to the invention, in which a single local adaptive window is utilized; 
     FIG. 4 illustrates some examples of window characteristics versus current pixel; 
     FIG. 5 is a block diagram representing a pixel-based sequential-to-parallel converter; 
     FIG. 6 a  is a graphic notation of a scalar product device; 
     FIG. 6 b  is a specific example of a scalar product device implementing two vectors each having three (3) components; 
     FIG. 7 illustrates a functional detailed diagram of the first basic embodiment of FIG. 1 using scalar product devices; 
     FIG. 8 is a general block diagram representing a second preferred embodiment of a spatial noise reducer according to the present invention, in the form of a parallel multiple local adaptive window spatial noise reducer; 
     FIG. 9 is a block diagram representing a third preferred embodiment of s spatial noise reducer according to the invention, which combines a single local adaptive window spatial noise reducer with a controllable noise variance estimator. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring now to FIG. 3 a first embodiment of the present invention in the form of a Single local Adaptive Window Spatial Noise Reducer (SAW-SNR) will be described. This basic configuration provides a segmentation-based MMSE spatial noise reducer for efficient edge preservation, and noise reduction in both homogenous or edges picture regions. The configuration can be also considered as a suitable combination of the Sigma and the MMSE filters. In the block diagram shown in FIG. 3, appropriate delays allowing signal synchronization required by the various operations of the signal processing are not represented, implementation of such delays being well known in the art. An example of such an implementation will be described later in reference to FIG. 7. A noisy image input signal g(x,y) at  301 , which is typically a luminance component of the image, is applied to the SAW-SNR generally designated at  318 . The couple (x,y) represents the current coordinates of a considered pixel. The input signal g(x,y) is a noisy version of an original image f(x,y). A noise component n(x,y) is supposed to be additive, zero mean and independent to the original image, i.e.: 
     
       
           g ( x,y )= f ( x,y )+ n ( x,y ).  (1) 
       
     
     The purpose of a noise reducer is to provide a cleaned output signal f*(x,y) which should be as close as possible to the original and unknown signal f(x,y). A usual criterion is the minimum of mean square error: 
     
       
           J= min  E (( f ( x,y )− f *( x,y )) 2 )  (2) 
       
     
     in which the image output signal f*(x,y) is a linear combination of the noisy observed image input g(x,y) and a local constant value: 
     
       
           f *( x,y )= a ( x,y ) ·g ( x,y ) +b ( x,y ).  (3) 
       
     
     Substituting equation (3) in equation (2) and minimizing the mean square error, the expression of f*(x,y) in equation (3) becomes: 
     
       
           f *( x,y )= K ( x,y ) ·g ( x,y )+(1− K ( x,y ))·μ( x,y )  (4a) 
       
     
     
       
           f *( x,y )=μ( x,y ) +K ( x,y )·( g ( x,y )−μ( x,y ))  (4b) 
       
     
     wherein μ(x,y) is the local image mean value of f(x,y) or that of g(x,y)and the weight K(x,y) is determined by: 
     
       
           K ( x,y )=min(0,(σ g   2 ( x,y )−σ n   2 ))/(σ g   2 ( x,y )).  (5) 
       
     
     In the latter equation, the symbol σ g (x,y) denotes the local standard deviation (SD) of the input image g(x,y), and σ n  corresponds in turn to that of the noise signal n(x,y). Substituting these results into equation (2) yields the minimum value of the performance indices: 
     
       
           J=J *=σ f   2 ( x,y )·σ n   2 /(σ f   2 ( x,y )+σ n   2 ).  (6) 
       
     
     In equation (6), the symbol σ f   2 (x,y) denotes the local variance of the clean image f(x,y). The two equations 4 and 5 imply the knowledge of the local mean μ(x,y) and the local SD σ g (x,y). Unfortunately, the latter values are generally unknown. They should be estimated by some suitable manner. 
     In the Lee&#39;s MMSE filter, these values are calculated in a local fixed dimension window around the central pixel (x,y) as follows 
     
       
         μ( x,y )=ΣΣ g ( i,j;x,y )/N T   (7) 
       
     
     and 
     
       
         σ g   2 ( x,y )=ΣΣ(( g ( i,j;x,y )−μ( x,y )) 2 )/N T   (8) 
       
     
     wherein the summation are done over all i and j in the given local window, g(i,j;x,y) being values of a group of noisy input image parallel signals, N T  denoting the pixel total number in the window. In homogenous regions where pixels are similar, equation (7) yields generally good results. However, in near edge or edge regions, the estimation given by equation (7), which does not take care of the non-stationary signal, becomes erroneous. In a similar manner, equation (8) can overestimate the signal variance in edge or near edge regions, the weight K(x,y) in equation (5) becomes unity (1) and the system given by equation (4) does not reduce any more noise in such cases. Moreover, the square values in equation (8) imply many expensive multipliers in an hardware implementation. 
     Meanwhile, in the Lee&#39;s Sigma filter, the weight K(x,y) is set to be fixed and equal to zero (0), which imply that the Sigma filter is always sub-optimum, and the mean value estimation is calculated only with selected similar intensity pixels in the window, that is: 
     
       
         μ( x,y ) =ΣΣw ( i,j;x,y )· g ( i,j;x,y ))/ N   (9) 
       
     
     
       
           N=ΣΣw ( i,j;x,y )  (10) 
       
     
     wherein the summation are done again over all i and j in the given local window, N bein a value of said selected-pixels count signal. However, in order to consider both homogenous and non-homogenous regions, the pixel intensities g(i,j;x,y) are weighted by window binary signals w(i,j;x, y) defined as follows:                w        (     i   ,     j   ;   x     ,   y     )       =     {           1   ,             if                          g        (     i   ,     j   ;   x     ,   y     )       -     g        (     x   ,   y     )                &lt;   T               0   ,         elsewhere                   (   11   )                                
     wherein g(x,y)=g(0,0;x,y) is the intensity of the window central and considered pixel, T being a predetermined threshold value. The suggested threshold value in Sigma filters is equal to 2σ n . The higher the threshold value T, the more noise is reduced, but at the expense of blurring true small image edges. If the threshold value T is small, equation (11) will leave unchanged isolated strong spot noise. 
     In order to combine the main advantages of Lee&#39;s MMSE and Sigma filters, the noisy input signal g(x,y) shown at  301  in FIG. 3, is firstly fed to a preliminary low pass (LP) filter  302 . The low pass filter  302  plays a very important role by partly reducing strong spot noise. Its output g*(x,y) allows robust segmentation in the presence of noise. For additive noise, it can be a simple filter with impulse response function lp(x,y) described by the following matrices:                lp        (     x   ,   y     )       =       [           1   /   9           1   /   9           1   /   9               1   /   9           1   /   9           1   /   9               1   /   9           1   /   9           1   /   9           ]                   or             (12a)                 lp        (     x   ,   y     )       =     [           1   /   16           2   /   16           1   /   16               2   /   16           4   /   16           2   /   16               1   /   16           2   /   16           1   /   16           ]             (12b)                                
     The filter output g*(x,y)  303  is sent to a first pixel-based serial-to-parallel converter (PB-SPC)  306  receiving the noisy low-spatial frequency image signal to generate a group of noisy low-spatial frequency image parallel signals associated with a locally considered pixel and according to predetermined pixels-window characteristics, which parallel signals generated at  303 ′ are fed to a pixel-based local window segmentation processor  304  that selects pixels in a local window which are similar to the considered current pixel. An implementation example of a PB-SPC will be described later with reference to FIG.  5 . The segmentation working with filtered image, yields at its output  320  a set of window binary signals w(i,j;x,y) defined now as:                w        (     i   ,     j   ;   x     ,   y     )       =     {           1   ,             if                            g   *          (     i   ,     j   ;   x     ,   y     )       -       g   *          (     x   ,   y     )                &lt;   T               0   ,         elsewhere                   (   13   )                                
     wherein: 
     g*(i,j;x,y) is the values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−k, . . . ,+l; j=−m, . . . ,+n, the window having dimensions of (k+l)×(m+n), k,l,m,n being appropriate positive integers; 
     g*(x,y) is the locally considered pixel value; and 
     T is a predetermined threshold value. 
     Since the noise component in the filtered image g*(x,y) is reduced, the threshold value T fed at an input  305  can be also reduced. Practically, the threshold value T can be set to σ n  or usually to a fixed value selected from 8 to 16 in an eight (8) bits video system, if noise is weak enough. The original noisy image signal g (x,y) at  301  is sent to a second PB-SPC  321  receiving the noisy input image signal to generate a group of noisy input image parallel signals g(i,j;x,y) associated with the locally considered pixel and according to the predetermined pixels-window characteristics. The parallel signals g(i,j;x,y) generated at the PBSPC output  322  connected to a line  323  and the window binary signals w(i,j;x,y) at  320 ′ are sent together to the mean value μ estimator  307  having an output  308  described by the above equations (9a) and (9b). In these equations, the term N is simply a count of selected pixels in the local window which are similar to the considered pixel (x,y) within the tolerance of threshold value T. A selected-pixels counter  327  with signal input w(i,j;x,y)  320  can be done by appropriate adders to realize equation (9b) above. The counter output signal at  328  represented in a few bits is sent to the mean value μ estimator  307 . The SAW-SNR further comprises a minimum-mean-square-error filter generally designated at  329  receiving the noisy input image parallel signals g(i,j;x,y) at  322 , the segmented local window parallel signals w(i,j;x,y) at  330 , the selected-pixels count signal N at  331  and the mean pixel value signal μ(x,y) at  332  to generate a noise-filtered output image signal f*(x,y) according to an input noise statistic signal at  314 . More specifically, the filter  329  comprises a statistical calculator  312  receiving the above signals g(i,j;x,y), w(i,j;x,y) and N to either generate a standard deviation (SD) signal or a variance signal at  313 . In order to reduce the inherent complexity of equation (8) above, the proposed calculator  312  is preferably a standard deviation calculator yielding a SD value σ g (x,y) at  313  estimated by absolute deviation mean as follows: 
     
       
         σ g ( x,y )= C *(ΣΣ( w ( i,j;x,y )·(| g ( i,j;x,y )−μ( x,y )|)))/ N.   (14) 
       
     
     In the latter equation, N is again the number of selected similar pixels in the local window. Moreover, depending on the noise distribution, an appropriate value for constant C can be chosen equal to about 1.25 for additive Gaussian noise, or to about 1.15 for additive uniform noise. The filter  329  further comprises a weight calculator  319 . The current SD σ g (x,y) at  313  as given by equation (14) and the noise input SD σ n  at  314  are preferably applied to the weight calculator  319  generating weight K(x,y) as already described by equation (5) above. Although less efficient, the statistical calculator  312  may also be a variance calculator implementing equation (8) to generate a variance value σ g   2 (x,y) in which case the noise input variance σ n   2  is fed to the weight calculator  319  to generate weight K(x,y) using same equation (5). In the case of white noise of constant variance σ g   2 , instead of a weight calculator  319 , a lookup table (LUT) is preferably used, which is built by pre-calculating necessary weights K(x,y). In the latter case, the value of constant C in equation (14) can be chosen fixed and the input noise variance σ n   2  represented by a few bits can be entered by an end-user to obtain the desired noise reduction level. 
     After proper delaying, the noisy image signal at  322  is applied in turn to an adder  309  included in the filter  329  through an input line  324 . The adder output signal at  325  representing the current difference (g(x,y)−μ(x,y)) is weighted by the current weight K(x,y) signal at  315  by a multiplier  310  included in the filter  329 . The multiplier output  316  and the mean value μ(x,y) are applied together to an adder  311  included in the filter  329  to yield the filtered image signal f*(x,y)  316  of the desired Single local Adaptive Window Spatial Noise Reducer (SAW-SNR)  318 . 
     It is worthwhile to note that, in addition to the usual noisy input g(x,y) at  301  and the filtered output f*(x,y) at  316 , the SAW-SNR  318  has also an input  300  for a window characteristics data (WIC) signal. This signal is used to select some window size and its relative position to the considered current pixel at the coordinates (x,y). In another words, the selected window is usually, but not absolutely necessary centered to the current pixel. Some pixel offsets or different window sizes can be useful for a more sophisticated spatial noise reducer, which will be described later according to a second preferred embodiment of the present invention. 
     Referring now to FIG. 4, there is illustrated a few examples of window characteristics relatively to the current pixel position. The window  401  is a 5×5 square one centered to the current pixel  400 , a centered square window being suitable for progressive image. The dotted window  402  is also a centered window of dimension 3×7, i.e., 3 lines and 7 columns, an horizontal rectangular window being suitable for interlaced signal. For the case of a single adaptive window spatial noise reducer, centered windows are recommended. Moreover, equations (7), (9) and (10) above show that, at least in a homogeneous region, the larger the window the better the result. FIG. 4 illustrates also two windows  403  and  404  that are not centered on the current pixel. The window  403  has one pixel being offset to the left, while the window  404  has one pixel being offset to the right. Horizontal offset windows are suitable for economic purpose in a Parallel Multiple local Adaptive Window Spatial Noise Reducer (Parallel M-AW-SNR), which will be described later in detail with reference to FIG.  8 . 
     Before referring to FIG. 7, which illustrates a functional implementation block diagram of the first embodiment in detail, in order to simplify and facilitate the presentation, let&#39;s consider now FIG. 6 that represents an example of a pixel-based sequential-to-parallel converter (PB-SPC) of 3×5 dimension. In order to create a local window for filtering or for segmentation, it is necessary to use a PB-SPC that converts a scanned image signal to multiple parallel signals corresponding to all pixel intensity values in the given window. The scanned image input signal g(x,y) at  501  is simply applied to appropriate line and pixel delays in series. For a window of 3×5 dimension, it takes at least two line delays  502 ,  504  and three (3) series of four (4) pixel delays  510 - 516 ,  520 - 526  and  530 - 536 , respectively. In this example, if the window is centered, the current pixel is located at an output  523 . The other output signals  501 ,  503 ,  505 ,  511 , . . . , 513 ,  533 , . . . , 537 , correspond to the current pixel neighbors in the window. Turning now to FIG. 6 a , there is illustrated a graphic notation of a scalar product (SP) from two vectors {right arrow over (a)}=(a 1 , a 2 , . . . ,a n ) at an input  611  and {right arrow over (b)}=(b 1 , b 2 , . . . ,b n ) at an input  612 , according to the following relation:              SP   =       (       a   →                   •                   b   →       )     =       ∑     i   =   1     n            a   i          b   i                   (   15   )                                
     wherein the summing is done over i for i=1,2, . . . ,n. In the notation, n is the component number of the vectors {right arrow over (a)} and {right arrow over (b)}. The scalar product (SP) at  616  is done by a scalar multiplier  613 . Some implementation details of the scalar multiplier are shown in FIG. 6 b . It consists of n (illustrated with n=3) parallel multipliers  613 - 1 ,  613 - 2 , . . . ,  613 -n and an adder  615  of n multiplier results  614 - 1 ,  614 - 2 , . . . , 614 -n. The inputs of the i th  multiplier  613 -i are the components a i ,  611 -i and b i ,  611 -i of the respective vectors {right arrow over (a)} and {right arrow over (b)}. 
     Referring now to FIG. 7, an implementation for the first embodiment of the proposed SAW-SNR basically described above with reference to in FIG. 3 will be now explained in detail. The noisy input image g(x,y) at  701  is applied to an input PB-SPC  702  of dimension 3×3 as part of the low-pass filter represented at  302  in FIG.  3 . The PB-SPC vector output at  717  is applied in turn to a SP  741  that also receives a vector of low pass filter coefficients at  740 . Assuming that equation (12a) above describes the low pass filter, all component values of the coefficient vector at  740  are equal to unity (1). The proportional term {fraction (1/9)} in equation (12a) can be further absorbed by the threshold value in equation (13) above. Meanwhile, the output  718  of the PB-SPC  702 , corresponding to the current pixel, is an appropriate delayed version of the input noisy image signal g(x,y). The SP output  703 , corresponding to the low pass output signal  303  in FIG. 3, is sent to a PB-SPC  704 , corresponding to the PB-SPC  306  in FIG. 3, of desired window dimension in order to get the pixel intensity vector at  705  of the given local window. A single input  706  represents the current filtered pixel intensity output signal g*(x,y). Let N T  being the window total pixel number and also the component number of the vector at  705  in which the signal component at  706  corresponds to the current considered pixel intensity. The vector at  705  and the current component at  706  are then applied to a vector adder  707  as illustrated by a double circle, which in fact includes N T  parallel adders, to calculate the N T  differences (g*(i,j;x,y)−g*(x,y)) as required by equation (13) above. The vector adder output  708  is applied to a vector full-wave rectifier  739  necessary now for calculating |g*(i,j;x,y)−g(x,y)| for all i and j in the window. For segmentation purpose, the rectifier output vector at  740  and the threshold vector at  709  are sent to a vector adder  710 , from which a resulting vector at output  711  is fed to a comparison detector  712 . The detector output vector {right arrow over (w)} at  713  composed of N T  binary components {w(i,j;x,y)} is sent now to three (3) SP  715 ,  724  and  728 . The SP  715  is simply provided as part of the selected similar pixel counter, and is provided with a second input  714  fed by a constant and unitary (1) vector. The SP output  716  represents the local selected similar pixel number N, corresponding to the current pixel intensity. Meanwhile, the delayed version of the input noisy signal g (x,y) at  718  is sent to a PB-SPC  719 , corresponding to the PB-SPC  321  in FIG. 3, of the same desired window dimension than that of PB-SPC  704 . The parallel output vector at  720  and the segmented window vector {right arrow over (w)} at  713  are applied together to the SP  724  that yields at its output  735  the summing value of similar pixel intensities in the local window. The total and local value signal at  735  and the local number N signal at  716  are applied to a ROM or LUT  737  to get a local mean μ at  721 . It is pointed out that a few bits can represent the value N. The ROM or LUT  737  is proposed to avoid an expensive division. In order to determine the local SD σ g  of the noisy input image, the vector of N T  parallel windowed noisy pixels at  720  and the obtained local mean μ at  721 ′ are applied also to a vector adder  722 . The vector adder output  723  is sent in turn to a vector full-wave rectifier  725  generating at  726  an output difference vector {|g(i,j;x,y)−μ(x,y)|} of N T  components. The difference vector signals and the segmented window vector {right arrow over (w)} signals are sent to the SP  728  that yields at the output  729  the weighted sum (ΣΣw(i,j;x,y)·(|g(i,j;x,y)−μ(x,y)|)) as necessary for equation (14) above. The multiplication by a constant C equal to about 1.25 for Gaussian noise and the division by N selected similar pixel number at  716  can be integrated in a ROM or LUT  731  generating at an output  743  an estimated local SD σ g  of the noisy input image, which is fed with the SD σ n  of the input noise at  730  to a weight calculator  742  implementing equation (5) above. The current calculated weight output K(x,y) at  732  is used to weigh the current difference (g(x,y)−μ(x,y)) at  727  via a multiplier  733 . The multiplier weighted output signal at  736  is sent to an adder  734 . The above weighted current difference signal at  736  and the local mean μ(x,y) at  721 , via an adder  734 , give the noise-filtered output image signal f*(x,y) at  738 . 
     Referring now to FIG. 8, a second embodiment of the present invention in the form of a Parallel Multiple local Adaptive Window Spatial Noise Reducer (M-AW-SNR) will be now described. Let g(x,y) be the noisy image input signal at  801 , which corresponds to the same input signal at  301  in FIG.  3 . The noisy signal is applied simultaneously to L parallel SAW-SNRs, respectively designated at  318 - 1 ,  318 - 2 , . . . and  318 -L. Each SAW-SNR  318 , which was basically described above with reference to FIG. 3, is working now with a distinct set of window characteristics WIC fed at inputs  300 - 1  to  300 -L. The SAW-SNR outputs,  316 - 1  to  316 -L, are added together via an adder  820  as part of an averaging unit  824 . The adder output  821  is divided by L and then rounded by a calculator  822  included in the averaging unit  824 . The result output  823  is also the desired final signal of the Parallel M-AW-SNR. It is to be understood that the L low pass filters included in the L parallel SAW-SNRs can be replaced by a single low pass filter  802  indicated in dotted lines in FIG.  8 . The proposed Parallel M-AW-SNR configuration can be justified by its performance. Let h i (x,y) be the i th  SAW-SNR output,  318 -i, defined as follows: 
     
       
           h   i ( x,y )= f ( x,y )+ r   i ( x,y )  (16) 
       
     
     The residual noisy component r i (x,y) is supposed to be non-correlated to the clean image f(x,y). This supposition is valid at least in edge regions where noise can remain after applying a SAW-SNR. The correlation between two components r i (x,y) and r j (x,y) resulted from two different but overlapped windows with similar size, is supposed equal to followings: 
     
       
           E ( r   i ( x,y )· r   j ( x,y ))=σ r   2 ·ρ ij   (17) 
       
     
     The term σ r   2  denotes the variance of the residual SAW-SNR noise, and ρ ij  is the normalized cross-correlation between noisy components r i (x,y) and r j (x,y). Let JPM and J denoting respectively the performance indices of a Parallel M-AW-SNR and a SAW-SNR. It can be shown that the improvement J/JPM is equal to: 
       J/JPM=L   2 /ΣΣρ ij &gt;1  (18) 
     The improvement has been verified experimentally even in the case of a SAW-SNR, which utilizes, as window, the union of all individual ones in a given Parallel M-AW-SNR. The proposed Parallel M-AW-SNR is an averaging configuration that can also be justified by its simplicity. In fact, it would be much more complicated to obtain an optimum linear combination with L SAW-SNRs output images. The Parallel M-AW-SNR configuration is composed of L parallel SAW-SNRs as previously described. It could be also possible to utilize SAW-SNRs in series (not shown). If the residual noise after each SAW-SNR is supposed to be still non-correlated to the clean image f(x,y), it can be theoretically shown that the performance indicia JSM associated with a Serial M-AW-SNR is given by the following equation: 
     
       
           JMS=σ   f   2 ( x,y )·σ n   2 ( x,y )/(σ f   2 ( x,y )+ L·σ   n   2 )  (19) 
       
     
     In equation (19), the terms σ f   2 (x,y) and σ n   2  denote respectively the local clean image variance and the original input noise variance. Hence, for the Serial M-AW-SNR, a theoretical improvement of: 
     
       
           J/JSM=(σ   f   2 ( x,y )+ Lσ   n   2 )/(σ f   2 ( x,y )+σ n   2 )&gt;1  (20) 
       
     
     over the single SAW-SNR is possible. However, this expression shows that, in edge regions, the improvement is not necessary appreciable. Moreover, serial configuration requires estimation of each individual residual noise, which imply a costly hardware implementation. Therefore, the Parallel M-AW-SNR is recommended over the Serial M-AW-SNR. 
     Turning now to FIG. 9, a third preferred embodiment of the present invention in the form of a Single local Adaptive Window Spatial Noise Reducer with controllable noise statistic estimator will be now described. The noisy image signal g(x,y) is applied at  301 , as usual, to the basic SAW-SNR  318  generating the noise-filtered output image signal f*(x,y) at  316 . At the same time, the noisy image is sent also to a High-Pass Two-Dimensional Filter HP2DF  901 , which extracts only very high vertical and horizontal frequency components in the input image. The filter band-pass bandwidth is chosen such that the filter output  902  contains mainly noise components, but not clean image. Justification for the choice of a HP2DF can be explained generally by the wide-band nature of noise. The utilize HP2DF is preferably a diamond filter, an example of impulse response function thereof being as follows:                hp2d        (     x   ,   y     )       =       [         0       0       0         -   1         0       0       0           0       0         -   3         8         -   3         0       0           0         -   3         16         -   25         16         -   3         0             -   1         8         -   25         32         -   25         8         -   1             0         -   3         16         -   25         16         -   3         0           0       0         -   3         8         -   3         0       0           0       0       0         -   1         0       0       0         ]     /   256             (   21   )                                
     The noisy horizontal/vertical high-spatial frequency image signal at  902  is fed to a PBSPC  911  to generate at output  913  a group of noisy horizontal/vertical high-spatial frequency image parallel signals associated with the locally considered pixel and according to the set of predetermined pixels-window characteristics. The BPSPC output  913  with the segmented local window parallel signals w(i,j;x,y) at  320  and the selected-pixels count signal Nat  328  available from the SAW-SNR  318  are sent to a statistic calculator  912  to generate a resulting noise statistic signal associated with the locally considered pixel. As explained before regarding the first preferred embodiment, while the statistic calculator  912  is preferably a SD calculator generating a SD resulting signal σ R (x,y) at  904 , it could be a variance calculator directly generating a variance resulting signal σ R   2 (x,y) at  904 . The calculator  912  is similar to the calculator  312  described before with reference to FIG. 3, but provided with a built-in mean value μ estimator. The calculator resulting signal at  904  is applied at the input of a look-up table LUT  905  that estimates, in turn, at its output  907 , a mean value of local noise input SD σ m (x,y) (or variance σ m   2 (x,y) ). The LUT input-output relationship between the two local standard deviations σ r (x,y) (or variance σ r   2 (x,y)) and σ m (x,y) (or variance σ m   2 (x,y)) can be described by the following method. Let consider the linear portion in Eq.5 expressed by: 
     
       
           K ( x,y )=(σ g   2 ( x,y )−σ n   2 ( x,y ))/(σ g   2 ( x,y ))  (5b) 
       
     
     wherein the unknown additive noise variance σ n   2 (x,y) is supposed now to be varying. It is thus necessary to pre-estimate this variance value for each pixel located at (x,y). 
     In many situations where the processing is well defined, such as for NTSC or PAL encoding/decoding and DCT based compression/decompression, an available original and clean test signal f(x,y) can be used for noise pre-learning. FIG. 9 illustrates partly a proposed configuration at  960  used for performing an off-line noise variance pre-estimation. The original test signal f(x,y) at  950  is applied to the above-mentioned processing at  951  which gives a test noisy image signal g(x,y) at  952 . The additive test noise signal n(x,y) at  954  is then obtained by the difference (g(x,y)−f(x,y)) provided by an adder  953  and is sent in turn to a statistic calculator  955  similar to the calculator  912 . The test noise SD σ m (x,y) (or the test noise variance σ m   2 (x,y)) estimation is done in the same context of that of the signal g(x,y) in the proposed SAW-SNR  318 , with the segmented window parallel signals w(i,j;x,y) at  320  and the selected-pixels count signal N at  328 . In words, for a considered pixel at (x,y), one obtains a pair of SD values (σ r (x,y),σ n (x,y)) (or a pair of variance values (σ r   2 (x,y),σ n   2 (x,y))). For the whole test picture or set of test pictures, a given value of σ r (orσ r   2 ) can have many resulted values of σ m (or σ m   2 ). In order to obtain a unique input-output relationship for the LUT  905 , it is necessary, for a given σ r (or σ r   2 ), to define a single value σ m  representing all possible values of σ m . For the preferred SD calculation, proposed estimations for σ m  are as follows: 
     
       
         σ m =mean(σ m , given a value of σ r );  (22) 
       
     
     or 
     
       
         σ m =mode(σ m , given a value of σ r )  (23) 
       
     
     The estimation (22) or (23) can be done then on an off-line basis by a data storage and estimation device  957 . The input-output result (σ r ,σ m ) at  904  and  958  respectively, permits the establishment of a pre-calculated LUT  905  for real time processing involving an unknown image. If the memory LUT  905  is large enough, some controllable bits can be fed at input  903  representing a learning or functional condition, for example for NTSC, PAL or 12 Mbit MPEG. The main requirement of the method is the prior knowledge of the processing to create the noisy image g(x,y) from the clean image f(x,y). The LUT output signal σ m (x,y) at  907  could be sent directly (not shown) to the input  314  of the SAW-SNR  318  as local SD of the input noise. However, the estimated value σ m (x,y) in equations 22 or 23 is only a representative one and is not necessary always good for all spatial varying image conditions. In such cases, the controllable noise statistic estimator preferably comprises a conditional weighting unit  909  which could be another LUT that receives the signal σ m (x,y) at  907  and which is controlled, in turn, by the output  910  of a condition detector  908 . The condition detector is driven by the input noisy image signal g(x,y) through line  301 ′. Depending on the application nature, The condition detector  908  classifies the current pixel as belonging to some specific regions such as edge regions, flat regions, near edge near flat regions, texture regions etc., and accordingly applies an appropriate weight or function at  910  to the mean value σ m (x,y) fed at  907  for each region. As an example, for the case of ringing noise, the conditional weighting output can be (½)·σ m (x,y) in contour or texture regions, 2·σ m (x,y) in near edge near flat regions, (¼)·σ m (x,y) in flat regions (to protect small details that does not necessarily create the ringing noise) and σ m (x,y) elsewhere. The conditional weighting output is then sent to the SAW-SNR input  314  as local SD (or variance) of the input noise. It is to be understood that, for specific noise such as the coding block effect, the proposed method may be used provided appropriate specific conditional techniques and data are applied.