CN102801928A - Image processing method and image processing equipment - Google Patents

Abstract

The invention embodiment provides an image processing method and image processing equipment. The method comprises the following steps of: extracting a first macro block from an image, wherein the size of the first macro block is 2n*2n, 2n is the number of pixel points, n is a natural number which is less than or equal to 4; and calculating a threshold var-thresh when an image is filtered; extracting a second macro block from the image, wherein the size of the second macro block is 2N*2N, wherein the 2N is the number of pixel points, and N is an integer which is greater than 4; calculating a mean value Avg1 and a mean square deviation Var1 of (M1*M1) or (M1*1) pixel points surrounding a pixel point P (i,j) which is in the second macro block and serves as a centre, wherein M1 is greater than 2n; and if the mean square deviation Var1 is less than var-thresh, replacing the corresponding P (i,j) with Avg1, thus finishing corresponding image processing. The image processing method and the image processing equipment provided by the embodiment of the invention can better protect image details, and also can effectively remove noises.

Description

Image processing method and image processing equipment

Technical field

The present invention relates to the Image Information Processing field, relate in particular to a kind of image processing method and image processing equipment.

Background technology

Digital picture usually receives imaging device, transmission equipment and outside and causes influences such as interference in collection, conversion and transmission course, thereby causes generally all containing noise in the image of actual acquisition.Therefore, filtering and noise reduction is exactly a requisite link in the Digital Image Processing.

In the process of using the camera acquisition image, common noise comprises white Gaussian noise, salt-pepper noise and multiplicative noise.Wherein, white Gaussian noise is the electronic noise that the result of random thermal motion owing to electronics in the resistive device produces.Remove noise method commonly used and generally comprise mean filter or two kinds of methods of medium filtering.These two kinds of methods are to eliminate noise method simply the most fast, but also have certain defective and deficiency:

Adopt the mean filter method to remove noise and can cause the fuzzy of image border, and adopt median filtering method to remove noise, its filter effect is stronger to the dependence of filter window size.

Summary of the invention

For solving in the image processing process problem of filtered noise fully, the embodiment of the invention provides a kind of image processing method and image processing apparatus.

The embodiment of the invention provides a kind of image processing method, comprising:

Extract first macro block in the image, the size of said first macro block is 2 ⁿ* 2 ⁿ, wherein 2 ⁿThe number of remarked pixel point, n is the natural number smaller or equal to 4, the threshold value var_thresh when calculating said image and carrying out Filtering Processing;

Extract second macro block in the image, the size of said second macro block is 2 ^N* 2 ^N, wherein 2 ^NThe number of remarked pixel point, N is the integer greater than 4;

With the pixel P in said second macro block _{(i, j)}Be the said P of center calculation _{(i, j)}(M1*M1) perhaps average Avg of (M1*1) individual pixel on every side ₁With mean square deviation Var ₁, M1 > wherein; 2 ⁿ, the number of M1 remarked pixel point;

If mean square deviation Var ₁Less than said var_thresh, then use said Avg ₁The P that replacement is corresponding _{(i, j)}, accomplish corresponding image processing.

The embodiment of the invention provides a kind of image processing equipment, comprising:

First extraction module is used for extracting first macro block of image, and the size of said first macro block is 2 ⁿ* 2 ⁿ, wherein 2 ⁿThe number of remarked pixel point, n is the natural number smaller or equal to 4, the threshold value var_thresh when calculating said image and carrying out Filtering Processing;

Second extraction module is used for extracting second macro block of image, and the size of said second macro block is 2 ^N* 2 ^N, wherein 2 ^NThe number of remarked pixel point, N is the integer greater than 4;

Computing module is used for getting a pixel P from second macro block that said second extraction module extracts _{(i, j)}, with said P _{(i, j)}Be the said P of center calculation _{(i, j)}(M1*M1) perhaps average Avg of (M1*1) individual pixel on every side ₁With mean square deviation Var ₁, M1 > wherein; 2 ⁿ, the number of M1 remarked pixel point;

Judge module is used for the mean square deviation Var that calculates when computing module ₁During less than the var_thresh that calculates in said first extraction module, use said Avg ₁The P that replacement is corresponding _{(i, j)}, accomplish corresponding image processing.

Image processing method that the embodiment of the invention provides and image processing equipment are though on the effect of image processing, still have certain dependence to the size of filter window.But it is few to work as image detail, and when not having marginal information, filter window can be suitably big; The image that comes out of filtering can be more level and smooth like this; Noise remove still less, abundant when image detail, filter window can be suitably a little bit smaller; In denoising, can accomplish better protection like this to details.Therefore, image processing method that the embodiment of the invention provides and image processing equipment in the better protect image detail, also can be removed noise effectively.

Description of drawings

Fig. 1 is the flow chart of the image processing method that provides of the embodiment of the invention;

Fig. 2 is the structural representation of the equipment that provides of the embodiment of the invention.

Embodiment

Below in conjunction with accompanying drawing the method and apparatus that the embodiment of the invention provided is carried out detailed description.

The embodiment of the invention provides a kind of image processing method, and is as shown in Figure 1, is a kind of image processing method of using the multiwindow adaptive wiener filter, comprising:

101, first macro block in the extraction image, the size of said first macro block is 2 ⁿ* 2 ⁿ, wherein 2 ⁿThe number of remarked pixel point, n is the natural number smaller or equal to 4, the threshold value var_thresh when calculating said image and carrying out Filtering Processing;

102, second macro block in the extraction image, the size of said second macro block is 2 ^N* 2 ^N, wherein 2 ^NThe number of remarked pixel point, N is the integer greater than 4;

In embodiments of the present invention, image processing equipment extracts first macro block and second macro block of image, and this image processing equipment can be a PC or server, is integrated with image processing software.The size of first macro block can be 2*2,4*4,8*8,16*16, and its unit is a pixel.The size of second macro block then should be bigger than first macro block, should be 32*32 at least, and wherein first macro block, second macro block have existed in the image, and image processing equipment directly extracts or reads and gets final product.

103, with the pixel P in said second macro block _{(i, j)}Be the said P of center calculation _{(i, j)}(M1*M1) perhaps average Avg of (M1*1) individual pixel on every side ₁With mean square deviation Var ₁, M1 > wherein; 2 ⁿ, the number of M1 remarked pixel point;

If 104 mean square deviation Var ₁Less than said var_thresh, then use said Avg ₁The P that replacement is corresponding _{(i, j)}, accomplish corresponding image processing.

In embodiments of the present invention, so-called image processing promptly with the corresponding pixel of average replacement after calculating, so just can be removed the noise in the image, makes image more level and smooth.

In another one embodiment of the present invention, this method can also comprise:

If 105 mean square deviation Var ₁More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M2*M2) perhaps average Avg of (M2*1) individual pixel on every side ₂With mean square deviation Var ₂, M2 > wherein; M1, if the number of M2 remarked pixel point is mean square deviation Var ₂Less than said var_thresh, then use said Avg ₂The P that replacement is corresponding _{(i, j)}, accomplish corresponding image processing; Perhaps

If 106 mean square deviation Var ₂More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M3*M3) perhaps average Avg of (M3*1) individual pixel on every side ₃With mean square deviation Var ₃, handle according to the method described above, wherein M3>M2, the number of M3 remarked pixel point.

In another one embodiment of the present invention, this method can also comprise:

If the corresponding Var of 107 maximum filter windows _iAlso more than or equal to said var_thresh, then with using DP _{(i, j)}Substitute P _{(i, j)}, accomplish the filtering of this pixel, wherein DP _{(i, j)}Computing formula be:

DP _{(i, j)}=P _{(i, j)}+ (Avg _i-P _{(i, j)}) * var_thresh/ (Var _i+ 1), Avg wherein _iRepresent the corresponding average of said maximum filter window.

Maximum filter window promptly progressively enlarges with P according to above-mentioned step _{(i, j)}Be the scope of the pixel at center, until can not expanded position, the pairing maximum magnitude in this position be maximum filter window, and its average is Avg _i, mean square deviation is Var _i

In embodiments of the present invention, said var_thresh can represent that its computing formula is with SVar:

$\mathrm{SVar}=\frac{1}{L}\underset{i=0}{\overset{L}{\mathrm{\Σ}}}\mathrm{Var},$

$\mathrm{Var}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\Σ}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\Σ}}}{(P(i,j)-\mathrm{Avg})}^2$

$\mathrm{Avg}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\Σ}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\Σ}}}P(i,j)$

Wherein, P _{(i, j)}The pixel of representing this first macro block,

WF is the wide of image, and WH is the height of image, the average of Avg remarked pixel point, Var remarked pixel point mean square deviation.

Need to prove: in embodiments of the present invention, get a less window (M1*M1) earlier and come filtering,, then use said Avg if satisfy above-mentioned condition ₁The P that replacement is corresponding _{(i, j)}If, do not satisfy, then use a bigger window (M2*M2) to come filtering again, if satisfy above-mentioned condition, then use said Avg ₂The P that replacement is corresponding _{(i, j)}If, do not satisfy then use a bigger window (M3*M3) to come filtering again, by that analogy, till window can not be big again.Wherein, M3>M2>M1, the value of M1, M2, M3 is 2 ⁿ, n is a natural number.

The method that adopts the embodiment of the invention to provide; The average and the mean square deviation of each pixel in can computed image, and compare with threshold value that the image adaptive Wiener filtering is handled be if the mean square deviation of this pixel is during less than threshold value; Then utilize this pixel of average replacement of this pixel; So just can filter out the point of all doubtful noises, guarantee the level and smooth of image, accomplish corresponding image processing.

Embodiment two:

With reference to accompanying drawing 1, the embodiment of the invention provides a kind of image processing method, comprising:

1, the adaptive threshold value (var_thresh) of calculating input image specifically comprises:

(1) obtain the size of input picture, the size of this image i.e. the area of this image, equals the height (WH) of wide (WF) * image of image, and wherein, WF, WH are the number of the pixel of image;

(2) in image, extract a macro block, be designated as first macro block, calculate the average Avg of this first macro block ₂₂, and according to Avg ₂₂Calculate the mean square deviation Var of this first macro block ₂₂, the size of first macro block can be 2*2,4*4,8*8,16*16 etc., and present embodiment is that 2*2 is that example describes with the size of first macro block, and wherein (2,4,8,16 refer to the number of pixel), its computing formula is:

${\mathrm{Avg}}_{22}=\frac{1}{2*2}\underset{j=0}{\overset{j=1}{\mathrm{\Σ}}}\underset{i=0}{\overset{i=1}{\mathrm{\Σ}}}P(i,j)$ (formula one)

Wherein, P _{(i, j)}The pixel of representing this first macro block, (wherein, the coordinate of i, j remarked pixel point, P _{(i, j)}Value can directly from image, get access to)

${\mathrm{Var}}_{22}=\frac{1}{2*2}\underset{j=0}{\overset{j=1}{\mathrm{\Σ}}}\underset{i=0}{\overset{i=1}{\mathrm{\Σ}}}{(P(i,j)-{\mathrm{Avg}}_{22})}^2$ (formula two)

(3) calculate the Var of all macro blocks in this image ₂₂Average SVar ₂₂, its computing formula does

${\mathrm{SVar}}_{22}=\frac{1}{L}\underset{i=0}{\overset{L}{\mathrm{\Σ}}}{\mathrm{Var}}_{22},$ Wherein $L=\frac{\mathrm{WF}}{2}\×\frac{\mathrm{WH}}{2}$

Wherein, SVar ₂₂Need use threshold value var_thresh when exactly this image being carried out the multiwindow adaptive wiener filter.

2, in this image, extract the another one macro block, be designated as second macro block.Each pixel on this second macro block is carried out the perhaps adaptive Wiener filtering of multiwindow of one dimension of two dimension, and the size of second macro block is the height (WH) of wide (WM) * image of piece, wherein WM>16, HM>16, the number of WM, the equal remarked pixel point of WH;

Pixel in second macro block is designated as P _{(i, j)}, need be to each pixel P in second macro block _{(i, j)}All do following flow processing:

A, with pixel P _{(i, j)}Be the center, calculate around it (M1*M1) perhaps average Avg of (M1*1) individual pixel ₁With mean square deviation Var ₁, M1 > wherein; 2, average Avg ₁With mean square deviation Var ₁Computing formula with reference to formula one and formula two;

B, comparison Var ₁With the size of the var_thresh of this image that calculates in the step 1, if Var ₁<var_thresh then uses the corresponding Avg of this filter window ₁Substitute P _{(i, j)}, accomplish the filtering of this pixel, otherwise, then carry out step C;

Wherein, the filtering of accomplishing this pixel is: use Avg ₁The pixel P of correspondence in the alternate image _{(i, j)}, rebuild image, be and accomplished filtering.

C, with pixel P _{(i, j)}Be the center, calculate around it (M2*M2) perhaps average Avg of (M2*1) individual pixel ₂With mean square deviation Var ₂, M2 > wherein; M1, average Avg ₂With mean square deviation Var ₂Computing formula with reference to formula one and formula two;

D, comparison Var ₂With the var_thresh size of this image that calculates in the step 1, if Var ₂<var_thresh then uses the corresponding Avg of this filter window ₂Substitute P _{(i, j)}, accomplish the filtering of this pixel, otherwise, then carry out step e;

E, with pixel P _{(i, j)}Be the center, calculate around it (M3*M3) perhaps average Avg of (M3*1) individual pixel ₃With mean square deviation Var ₃, M3 > wherein; M2, average Avg ₃With mean square deviation Var ₃Computing formula with reference to formula one and formula two;

F, comparison Var ₃With the size of the var_thresh of this image that calculates in the step 1, if Var ₃<var_thresh then uses the corresponding Avg of this filter window ₃Substitute P _{(i, j)}, accomplish the filtering of this pixel, otherwise, then carry out step G;

G, according to steps A or step C, perhaps the method in the step e repeats to calculate, up to calculating with pixel P _{(i, j)}Be the center, (Ma*Ma) perhaps average Avg of (Ma*1) individual pixel around it _aWith mean square deviation Var _a, satisfy Var _a<the condition of var_thresh is then used the corresponding Avg of this filter window _aSubstitute P _{(i, j)}, accomplish the filtering of this pixel, if up to calculating the maximum corresponding Avg of a filter window _nAnd Var _nDo not satisfy Var yet _n<the condition of var_thresh is then carried out step H;

H, calculating DP _{(i, j)}, use DP _{(i, j)}Substitute P _{(i, j)}, accomplish the filtering of this pixel, wherein DP _{(i, j)}Computing formula be:

DP _(i，j)=P _(i，j)+(Avg _i-P _(i，j))×var_thresh/(Var _i+1)

The image processing method that adopts the embodiment of the invention to provide can be replaced the pixel of doubtful noise in the image, has guaranteed the level and smooth of image, effectively removes noise.

Embodiment three:

With reference to accompanying drawing 2, the embodiment of the invention provides a kind of image processing equipment, comprising:

First extraction module 201 is used for extracting first macro block of image, and the size of said first macro block is 2 ⁿ* 2 ⁿ, wherein 2 ⁿThe number of remarked pixel point, n is the natural number smaller or equal to 4, the threshold value var_thresh when calculating said image and carrying out filtering;

Second extraction module 202 is used for extracting second macro block of image, and the size of said second macro block is 2 ^N* 2 ^N, wherein 2 ^NThe number of remarked pixel point, N is the integer greater than 4;

Computing module 203 is used for getting a pixel P from second macro block that said second extraction module 202 extracts _{(i, j)}, with said P _{(i, j)}Be the said P of center calculation _{(i, j)}(M1*M1) perhaps average Avg of (M1*1) individual pixel on every side ₁With mean square deviation Var ₁, M1 > wherein; 2 ⁿ, the number of M1 remarked pixel point;

Judge module 204 is used for the mean square deviation Var that calculates when computing module 203 ₁During less than the var_thresh that calculates in said first extraction module 201, use said Avg ₁The P that replacement is corresponding _{(i, j)}, accomplish corresponding filtering.

The image processing equipment that the embodiment of the invention provided also comprises:

Said judge module is if also be used for mean square deviation Var ₁More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M2*M2) perhaps average Avg of (M2*1) individual pixel on every side ₂With mean square deviation Var ₂, M2 > wherein; M1, if the number of M2 remarked pixel point is mean square deviation Var ₂Less than said var_thresh, then use said Avg ₂The P that replacement is corresponding _{(i, j)}, accomplish corresponding image processing; Perhaps, if mean square deviation Var ₂More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M3*M3) perhaps average Avg of (M3*1) individual pixel on every side ₃With mean square deviation Var ₃, calculate again according to the method described above, wherein M3>M2, the number of M3 remarked pixel point.

The image processing equipment that the embodiment of the invention provided also comprises:

Said judge module is if also be used for the corresponding Var of maximum filter window _iAlso more than or equal to said var_thresh, then with using DP _{(i, j)}Substitute P _{(i, j)}, accomplish the filtering of this pixel, wherein DP _{(i, j)}Computing formula be:

DP _{(i, j)}=P _{(i, j)}+ (Avg _i-P _{(i, j)}) * var_thresh/ (Var _i+ 1), Avg wherein _iRepresent the corresponding average of said maximum filter window.

In embodiments of the present invention, said var_thresh can represent that its computing formula is with SVar:

$\mathrm{SVar}=\frac{1}{L}\underset{i=0}{\overset{L}{\mathrm{\&Sigma;}}}\mathrm{Var},$

$\mathrm{Var}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\&Sigma;}}}{(P(i,j)-\mathrm{Avg})}^2$

$\mathrm{Avg}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\&Sigma;}}}P(i,j)$

Wherein, P _{(i, j)}The pixel of representing this first macro block, WF is the wide of image, and WH is the height of image.

The image processing equipment that the embodiment of the invention provided is corresponding fully with the image processing method that embodiment one, two is provided, and present embodiment is not described detailed place, please with reference to the description among other embodiment.

More than be some preferred implementation of the embodiment of the invention; Anyone is under the prerequisite of skilled; Do not deviating from spirit of the present invention and do not exceeding under the prerequisite of the technical scope that the present invention relates to, can do various replenishing and modification the details that the present invention describes.Protection scope of the present invention is not limited to the cited scope of embodiment, and protection scope of the present invention is as the criterion with claim.

Claims (8)

1. an image processing method is characterized in that, comprising:

Extract first macro block in the image, the size of said first macro block is 2 ⁿ* 2 ⁿ, wherein said 2 ⁿThe number of remarked pixel point, n is the natural number smaller or equal to 4, the threshold value var_thresh when calculating said image and carrying out Filtering Processing;

Extract second macro block in the said image, the size of said second macro block is 2 ^N* 2 ^N, wherein said 2 ^NThe number of remarked pixel point, N is the integer greater than 4;

With the pixel P in said second macro block _{(i, j)}Be the said P of center calculation _{(i, j)}(M1*M1) perhaps average Avg of (M1*1) individual pixel on every side ₁With mean square deviation Var ₁, M1 > wherein; 2 ⁿ, the number of M1 remarked pixel point;

If mean square deviation Var ₁Less than said var_thresh, then use said Avg ₁Replace said P _{(i, j)}, accomplish corresponding image processing.

2. the method for claim 1 is characterized in that, also comprises:

If said mean square deviation Var ₁More than or equal to said var_thresh, then with said P _{(i, j)}Be the said P of center calculation _{(i, j)}(M2*M2) perhaps average Avg of (M2*1) individual pixel on every side ₂With mean square deviation Var ₂, M2 > wherein; M1, the number of M2 remarked pixel point;

If said mean square deviation Var ₂Less than said var_thresh, then use said Avg ₂Replace said P _{(i, j)}, accomplish corresponding image processing;

Perhaps

If said mean square deviation Var ₂More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M3*M3) perhaps average Avg of (M3*1) individual pixel on every side ₃With mean square deviation Var ₃, handle according to the method described above, wherein M3>M2, the number of M3 remarked pixel point.

3. method according to claim 2 is characterized in that, also comprises:

If the Var that maximum filter window is corresponding _iAlso, then use DP more than or equal to said var_thresh _{(i, j)}Replace said P _{(i, j)}, accomplish corresponding image processing, wherein said DP _{(i, j)}Computing formula be:

DP _{(i, j)}=P _{(i, j)}+ (Avg _i-P _{(i, j)}) * var_thresh/ (Var _i+ 1), Avg wherein _iRepresent the corresponding average of said maximum filter window.

4. like the arbitrary described method of claim 1 to 3, it is characterized in that said var_thresh representes that with SVar its computing formula is:

$\mathrm{SVar}=\frac{1}{L}\underset{i=0}{\overset{L}{\mathrm{\&Sigma;}}}\mathrm{Var};$

$\mathrm{Var}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\&Sigma;}}}{(P(i,j)-\mathrm{Avg})}^2;$

$\mathrm{Avg}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\&Sigma;}}}P(i,j);$

Wherein, said P _{(i, j)}The pixel of representing this first macro block,

WF representes the wide of said image, and WH representes the height of said image.

5. an image processing equipment is characterized in that, comprising:

First extraction module is used for extracting first macro block of image, and the size of said first macro block is 2 ⁿ* 2 ⁿ, wherein 2 ⁿThe number of remarked pixel point, n is the natural number smaller or equal to 4, the threshold value var_thresh when calculating said image and carrying out Filtering Processing;

Second extraction module is used for extracting second macro block of said image, and the size of said second macro block is 2 ^N* 2 ^N, wherein 2 ^NThe number of remarked pixel point, N is the integer greater than 4;

Computing module is used for getting a pixel P from second macro block that said second extraction module extracts _{(i, j)}, with said P _{(i, j)}Be the said P of center calculation _{(i, j)}(M1*M1) perhaps average Avg of (M1*1) individual pixel on every side ₁With mean square deviation Var ₁, M1 > wherein; 2 ⁿ, the number of M1 remarked pixel point;

Judge module is used for the mean square deviation Var that calculates when said computing module ₁During less than the var_thresh that calculates in said first extraction module, use said Avg ₁Replace said P _{(i, j)}, accomplish corresponding image processing.

6. equipment as claimed in claim 5 is characterized in that,

Said judge module also is used for as said mean square deviation Var ₁More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M2*M2) perhaps average Avg of (M2*1) individual pixel on every side ₂With mean square deviation Var ₂, M2 > wherein; M1, if the number of M2 remarked pixel point is mean square deviation Var ₂Less than said var_thresh, then use said Avg ₂The P that replacement is corresponding _{(i, j)}, accomplish corresponding image processing; Perhaps, if mean square deviation Var ₂More than or equal to said var_thresh, then with P _{(i, j)}Be the said P of center calculation _{(i, j)}(M3*M3) perhaps average Avg of (M3*1) individual pixel on every side ₃With mean square deviation Var ₃, then handle according to the method described above, wherein M3>M2, the number of M3 remarked pixel point.

7. equipment as claimed in claim 6 is characterized in that,

Said judge module is if also be used for the corresponding Var of maximum filter window _iMore than or equal to said var_thresh, then with using DP _{(i, j)}Replace said P _{(i, j)}, accomplish corresponding image processing, wherein said DP _{(i, j)}Computing formula be:

DP _{(i, j)}=P _{(i, j)}+ (Avg _i-P _{(i, j)}) * var_thresh/ (Var _i+ 1), Avg _iRepresent the corresponding average of said maximum filter window.

8. like the arbitrary described equipment of claim 5 to 7, it is characterized in that said var_thresh representes that with SVar its computing formula is:

$\mathrm{SVar}=\frac{1}{L}\underset{i=0}{\overset{L}{\mathrm{\&Sigma;}}}\mathrm{Var};$

$\mathrm{Var}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\&Sigma;}}}{(P(i,j)-\mathrm{Avg})}^2;$

$\mathrm{Avg}=\frac{1}{2^n*2^n}\underset{j=0}{\overset{j=2^n-1}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{i=2^n-1}{\mathrm{\&Sigma;}}}P(i,j);$

Wherein, P _{(i, j)}The pixel of representing this first macro block,

WF representes the wide of said image, and WH representes the height of said image.

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