Apparatus and method for adaptively reducing noise in a noisy input image signal

• US 6,633,683 B1
• Filed: 06/26/2000
• Issued: 10/14/2003
• Est. Priority Date: 06/26/2000
• Status: Expired due to Term

• Current Assignee: Grass Valley Canada (Belden Inc.)
• Original Assignee: Miranda Technologies Limited (Belden Inc.)
• Inventors: Viet, Ha Do, Dinh, Chon Tam Le, Kieu, Cong Toai
• Primary Examiner(s): Mehta, Bhavesh M.
• Assistant Examiner(s): Kassa, Yosef
• Application Number:
  US09/603,364
• Time in Patent Office: 1,205 Days
• Field of Search: 382/190, 382/209, 382/210, 382/211, 382/217, 382/218, 382/219, 382/252, 382/255, 382/260, 382/261, 382/262, 382/263, 382/264, 382/265, 382/266, 382/269, 382/272, 382/273, 382/274, 382/275, 382/278, 382/282, 382/284, 382/286, 382/287, 382/294, 382/309, 382/310, 345/611, 345/613, 345/616, 345/617, 345/618, 345/631, 348/466, 348/606, 348/610, 348/625, 358/3.05, 358/461, 358/452, 358/453, 358/463, 358/512
• US Class Current: 382/260
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06V 10/30 Noise filtering
• CPC Class Codes: H04N 5/21 Circuitry for suppressing or minimising disturbance, e.g. moiré or halo

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.

Claims

1. An adaptive apparatus for spatially reducing noise in a noisy input image signal comprising:

a low-pass filter receiving said noisy input image signal to generate a noisy low-spatial frequency image signal;

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;

a counter generating a selected-pixels count signal;

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;

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;

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.

2. An apparatus according to claim 1, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers;

g*(x,y) is said locally considered pixel value; and

T is a predetermined threshold value.

3. An apparatus according to claim 2, wherein a value of said mean pixel value signal is given by:

4. An apparatus according to claim 1, wherein said input noise statistic signal is an input noise variance signal, said minimum-mean-square-error filter including:

a variance calculator for generating an estimated variance signal from said noisy input image parallel signals, said segmented local window parallel signals, said selected-pixels count signal and said mean pixel value signal;

a weight calculator for generating a weight signal from the estimated variance signal and the input noise variance signal;

a first adder having an inverting input receiving said mean pixel value signal and a positive input receiving the noisy input signal associated with the locally considered pixel to generate a difference signal;

a multiplier receiving the weight signal and the difference signal to generate a weighted difference signal;

a second adder having a first positive input receiving the mean pixel value signal and a second positive input receiving the weighted difference signal to generate said noise-filtered output image signal.

5. An apparatus according to claim 1, wherein said input noise statistic signal is an input noise standard deviation signal, said minimum-mean-square-error filter including:

a standard deviation calculator for generating an estimated standard deviation signal from said noisy input image parallel signals, said segmented local window parallel signals, said selected-pixels count signal and said mean pixel value signal;

a weight calculator for generating a weight signal from the estimated standard deviation signal and the input noise standard deviation signal;

a first adder having an inverting input receiving said mean pixel value signal and a positive input receiving the noisy input signal associated with the locally considered pixel to generate a difference signal;

a multiplier receiving the weight signal and the difference signal to generate a weighted difference signal;

a second adder having a first positive input receiving the mean pixel value signal and a second positive input receiving the weighted difference signal to generate said noise-filtered output image signal.

6. An apparatus according to claim 5, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers;

g*(x,y) is said locally considered pixel value;

T is a predetermined threshold value;

wherein said mean pixel value signal is defined by;

7. 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 said noisy input image signal to generate a corresponding pre-filtered output image signal and an averaging unit receiving each said pre-filtered output image signal at a corresponding positive input thereof to generate a noise-filtered output image signal, wherein each said noise reducer comprises;

a low-pass filter receiving said noisy input image signal to generate a noisy low-spatial frequency image signal;

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;

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;

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;

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;

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 said pre-filtered output image signal according to an input noise statistic signal.

9. An apparatus according to claim 7 or 8, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers characterizing said distinct set of predetermined pixels-window characteristics;

g*(x,y) is said locally considered pixel value;

T is a predetermined threshold value; and

wherein a value of said mean pixel value signal is given by;

8. An adaptive apparatus for spatially reducing noise in a noisy input image signal comprising:

a low-pass filter receiving said noisy input image signal to generate a noisy low-spatial frequency image signal;

a plurality of parallel-connected adaptive spatial noise reducers each presenting a distinct set of predetermined pixels-window characteristics, each said noise reducer receiving said noisy low-spatial frequency image signal to generate a corresponding pre-filtered output image signal and an averaging unit receiving each said pre-filtered output image signal at a corresponding positive input thereof to generate a noise-filtered output image signal, wherein each said noise reducer 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 said set of 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;

a counter generating a selected-pixels count signal;

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;

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;

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 said pre-filtered output image signal according to an input noise statistic signal.

10. 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 said noisy input image signal to generate a noisy low-spatial frequency image signal;

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;

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;

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;

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;

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; and

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;

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;

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;

a noise statistic estimator unit generating said input noise statistic signal from the resulting noise statistic signal.

11. An apparatus according to claim 10, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers;

g* (x,y) is said locally considered pixel value; and

T is a predetermined threshold value.

12. An apparatus according to claim 11, wherein a value of said mean pixel value signal is given by:

13. An apparatus according to claim 10, wherein said input noise statistic signal is an input noise variance signal, said controllable noise statistic estimator is a controllable noise variance estimator, said statistic calculator is a variance calculator, said resulting noise statistic signal is a resulting noise variance signal, said noise statistic estimator unit is a noise variance estimator unit.

14. An apparatus according to claim 13, wherein said noise variance estimator includes:

a noise mean variance estimator receiving said input resulting noise variance signal to generate a mean variance signal;

a condition detector receiving said noisy input image signal to detect a spatial characteristic of said locally considered pixel;

a weighting device operatively connected to the condition detector for combining the mean variance signal with a weight value corresponding to a detected spatial characteristic to generate said input noise variance signal.

15. An apparatus according to claim 10, wherein said input noise statistic signal is an input noise standard deviation signal, said controllable noise statistic estimator is a controllable noise standard deviation estimator, said statistic calculator is a standard deviation calculator, said resulting noise statistic signal is a resulting noise standard deviation signal, said noise statistic estimator unit is a noise standard deviation estimator unit.

16. An apparatus according to claim 15, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers;

g*(x,y) is said locally considered pixel value;

T is a predetermined threshold value;

wherein said mean pixel value signal is defined by;

17. An apparatus according to claim 15, wherein said noise standard deviation estimator includes:

a noise mean standard deviation estimator receiving said input resulting noise standard deviation signal to generate a mean standard deviation signal;

a condition detector receiving said noisy input image signal to detect a spatial characteristic of said locally considered pixel;

a weighting device operatively connected to the condition detector for combining the mean standard deviation signal with a weight value corresponding to a detected spatial characteristic to generate said input noise standard deviation signal.

18. An adaptive method for spatially reducing noise in a noisy input image signal comprising the steps of:

i) filtering said 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.

19. A method according to claim 18, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)x (m+n), k,l,m,n being appropriate positive integers;

g*(x,y) is said locally considered pixel value; and

T is a predetermined threshold value.

20. A method according to claim 19, wherein a value of said mean pixel value signal is given by:

21. A method according to claim 18, wherein said input noise statistic signal is an input noise variance signal, said minimum-mean-square-error algorithm including the steps of:

a) generating an estimated variance signal from said noisy input image parallel signals, said segmented local window parallel signals, said selected-pixels count signal and said mean pixel value signal;

b) generating a weight signal from the estimated variance signal and the input noise variance signal;

c) subtracting said mean pixel value signal from the noisy input signal associated with the locally considered pixel to generate a difference signal;

d) multiplying the weight signal with the difference signal to generate a weighted difference signal;

e) adding the mean pixel value signal and the weighted difference signal to generate said noise-filtered output image signal.

22. A method according to claim 18, wherein said input noise statistic signal is an input noise standard deviation signal, said minimum-mean-square-error algorithm including the steps of:

a) generating an estimated standard deviation signal from said noisy input image parallel signals, said segmented local window parallel signals, said selected-pixels count signal and said mean pixel value signal;

b) generating a weight signal from the estimated standard deviation signal and the input noise variance signal;

c) subtracting said mean pixel value signal from the noisy input signal associated with the locally considered pixel to generate a difference signal;

d) multiplying the weight signal with the difference signal to generate a weighted difference signal;

e) adding the mean pixel value signal and the weighted difference signal to generate said noise-filtered output image signal.

23. A method according to claim 18, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers;

g*(x,y) is said locally considered pixel value;

T is a predetermined threshold value;

wherein said mean pixel value signal is defined by;

24. An adaptive method for spatially reducing noise in a noisy input image signal comprising the steps of:

i) filtering said 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.

25. A method according to claim 24, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers characterizing said distinct set of predetermined pixels-window characteristics;

g*(x,y) is said locally considered pixel value;

T is a predetermined threshold value; and

wherein a value of said mean pixel value signal is given by;

26. An adaptive method for spatially reducing noise in a noisy input image signal comprising:

i) filtering said 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) 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.

27. A method according to claim 26, wherein values of said segmented local window parallel signals are given by:

$w<br><br>\left(i,j;<br><br>x,y\right)=\{\begin{array}{cc}1,& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>g^{*}<br><br>\left(i,j;<br><br>x,y\right)-g^{*}<br><br>\left(x,y\right)<br><br><<br><br>T\\ 0,& \mathrm{elsewhere}\end{array}$

wherein;

g*(i,j;

x,y) is said values of the noisy low-spatial frequency image parallel signals associated with pixels included within the window with i=−

k, . . . ,+l;

j=−

m, . . . ,+n, said window having dimensions of (k+l)×

(m+n), k,l,m,n being appropriate positive integers;

g*(x,y) is said locally considered pixel value; and

T is a predetermined threshold value.

28. A method according to claim 27, wherein a value of said mean pixel value signal is given by: