CN106447629B - Non-local mean radar imaging coherent spot suppressing method based on ratio distance - Google Patents
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Abstract
The present invention provides a kind of non-local mean radar imaging coherent spot suppressing method based on ratio distance, comprising: reads synthetic aperture radar coherent spot image；Set corresponding search window and similitude window size；Any two similitude window ratio distance is determined according to the gray value in any two similitude window；The gray value ratio of any two similitude window center point is determined according to the gray value of the central point of any two similitude window；The space length of any two similitude window center point is determined according to the space coordinate of the central point of any two similitude window；The weighted value of pixel in two similitude windows is calculated according to the ratio distance, the central point gray value ratio and the central point space length；The estimated value of current pixel point is estimated by the weighted value.The present invention realizes effective inhibition coherent speckle noise, moreover it is possible to effectively keep edge, improve performance indicator, and visual more preferable.
Description
Technical field
The present embodiments relate to radar imaging coherent spots to inhibit field more particularly to a kind of non-office based on ratio distance
Portion's mean value radar imaging coherent spot suppressing method.
Background technique
In traditional non-local mean algorithm, use the Euclidean distance of image block gray value as calculate similitude no longer
Suitable for multiplying property model, and pass through stringent theory deduction it is found that ratio distance is more suitable for multiplying property model, is able to maintain pixel
Between true relevance.But when ratio distance being utilized to calculate similitude, distance is bigger closer to 1 similitude, and from 1
Remoter similitude is smaller, therefore cannot be directly by ratio distance as the module for measuring two image block similarities.
Traditional coherent speckle suppression method includes airspace class, small echo, anisotropy parameter and non-local mean method.
Airspace class speckle suppression method occurred smoothly, rejection being caused to reduce in strong edge and texture region；It is small
Wave and anisotropy parameter Speckle reduction part interference block can occur in radar image, influence visuality；It is non local at present equal
Value method only passes through ratio distance metric similitude, and Speckle reduction performance is not sufficiently stable.
Summary of the invention
The embodiment of the present invention provides a kind of non-local mean radar imaging coherent spot suppressing method based on ratio distance, with
Overcome the above problem.
A kind of non-local mean radar imaging coherent spot suppressing method based on ratio distance of the present invention, comprising:
Read synthetic aperture radar coherent spot image；
Set corresponding search window and similitude window size；
According to the gray value in any two similitude window determine any two similitude window ratio away from
From；
Any two similitude window is determined according to the gray value of the central point of any two similitude window
The gray value ratio of central point；
Any two similitude window is determined according to the space coordinate of the central point of any two similitude window
The space length of mouth central point；
Two phases are calculated according to the ratio distance, the central point gray value ratio and the central point space length
Like the weighted value of pixel in property window；
The estimated value of current pixel point is estimated by the weighted value.
Further, the gray value according in similitude window size and the similitude window determines described two
Similitude window ratio distance, comprising:
Using formula
Calculate two image block ratio distances, wherein the W is the size of similitude window, wherein NiWith NjPoint
It is not with pixel QiWith QjCentered on image block, andWithIt is then two image blocks in observed image f, D1 (i, j)
For two image block ratio distances, (i, j) is respectively the position coordinates of any two image block central pixel point, and M is any figure
As pixel number in block.
Further, the gray value of the central point according to any two similitude window determines described any two
The gray value ratio of central point between a similitude window, comprising:
Using formula
Calculate the gray value ratio between two central pixel points, wherein (x, y) and (p, q) is image block respectively
NiWith NjCentral pixel point position, D2 (i, j) is the gray value ratio between two central pixel points.
Further, any two are determined according to the space coordinate of the central point of any two similitude window
The space length of similitude window center point, comprising:
Using formula
D3 (i, j)=(x-p)2+(y-q)2 (3)
Determine the space length of any two similitude window center point, wherein (x, y) and (p, q) is two phases respectively
Like the center position coordinate of property window, D3 (i, j) is the space length of two similitude window center points.
Further, described according to the ratio distance, the central point gray value ratio and the center space of points
Distance calculates weighted value, comprising:
Using formula
Calculate the weighted value of pixel in two similitude windows, wherein described h1, h2 and h3 are control filtering journey respectively
The variable-definition for controlling filter strength is h1=h2=h3=kL σ by the variable of degree, and L is the view number of image, and k is constant, and σ is
The standard deviation of image, w (i, j) are the weighted value of pixel in two similitude windows.
Ratio distance of the invention can robustly be adapted to multiplying property model, by the way that ratio distance is combined image block
Central point gray value information and spatial positional information preferably to calculate the similitude between image block.The present invention has been not only able to
The inhibition coherent speckle noise of effect, moreover it is possible to effectively keep edge, improve performance indicator, and visual more preferable.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without any creative labor, can be with
It obtains other drawings based on these drawings.
Fig. 1 is that the present invention is based on the non-local mean radar imaging coherent spot suppressing method flow charts of ratio distance；
Fig. 2 is three kinds of location drawing picture block schematic diagrames of the invention；
Fig. 3 is three kinds of location drawing picture block schematic diagrames of the invention；
Fig. 4 is that the present invention is based on the Speckle reduction comparison diagrams of composograph
Fig. 5 is that the present invention is based on the Speckle reduction comparison diagrams of true radar image；
Fig. 6 is that the equivalent number of composograph of the present invention and the edge based on ratio of averages keep angle value with h1 change curve
Figure；
Fig. 7 is that the equivalent number of the true radar image of the present invention keeps angle value to change with h1 with the edge based on ratio of averages
Curve graph.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Fig. 1 is that the present invention is based on the non-local mean radar imaging coherent spot suppressing method flow chart of ratio distance, such as Fig. 1
Shown, the method for the present embodiment may include:
Step 101 reads synthetic aperture radar coherent spot image；
Step 102, the corresponding search window of setting and similitude window size；
Step 103 determines any two similitude window according to the gray value in any two similitude window
Mouth ratio distance；
Step 104 determines any two phase according to the gray value of the central point of any two similitude window
Like the gray value ratio of property window center point；
Step 105 determines any two according to the space coordinate of the central point of any two similitude window
The space length of similitude window center point；
Step 106, according to ratio distance, the central point gray value ratio and the central point space length meter
Calculate the weighted value of pixel in two similitude windows；
Step 107, the estimated value that current pixel point is estimated by the weighted value.
Further, the gray value according in similitude window size and the similitude window determines described two
Similitude window ratio distance, comprising:
Using formula
Calculate two image block ratio distances, wherein the W is the size of similitude window, wherein NiWith NjPoint
It is not with pixel QiWith QjCentered on image block, andWithIt is then two image blocks in observed image f, D1 (i, j)
For two image block ratio distances, (i, j) is respectively the position coordinates of any two image block central pixel point, and M is any figure
As pixel number in block.
Specifically, the meaning of defined formula (1) is, in order to which can the range formula of analytical formula (1) be suitable for multiplying
Property model, so we adjust the distance now, the mathematic expectaion of formula D1 (i, j) carries out mathematical derivation, and derivation process is as follows:
Here the assumed condition that we use are as follows:We this
In formula (2) derived into following expression:
Equally, it is 1 that coherent speckle noise v, which is obedience mean value, and variance isRandom noise, so,
Formula (4) is brought into formula (3), is obtained:
Formula (5) presents whether in homogeneous region or in heterogeneous areas, observed image f and is not polluted by coherent spot
True picture u between corresponding image block the mathematic expectaion of distance remain identical similitude between pixel, have more
Good robustness and validity.
Further, the gray value of the central point according to any two similitude window determines described any two
The gray value ratio of central point between a similitude window, comprising:
Using formula
Calculate the gray value ratio between two central pixel points, wherein (x, y) and (p, q) is image block respectively
NiWith NjCentral pixel point position, D2 (i, j) is the gray value ratio between two central pixel points.
Further, any two are determined according to the space coordinate of the central point of any two similitude window
The space length of similitude window center point, comprising:
Using formula
D3 (i, j)=(x-p)2+(y-q)2 (7)
Determine the space length of any two similitude window center point, wherein (x, y) and (p, q) is two phases respectively
Like the center position coordinate of property window, D3 (i, j) is the space length of two similitude window center points.
Specifically, as image block NiWith image block NjWhen completely similar, new distance D1 (i, j)=0.If two images
Block is more dissimilar, then distance D1 (i, j) is bigger.Distance D1 can be directly used for the weight computing of two image blocks, but in order to improve
In addition the accuracy of edge pixel point estimate increases two conditions: the gray value of central point between two similitude windows
The space length D3 of ratio D2 and two similitude window center points, are illustrated below with emulating image.
As shown in Fig. 2, there is two homogeneous regions of A, B in figure, 1,2,3 represent three image blocks, wherein each piece of centre
Dot represents the central pixel point of the block, and No. 1 image block is completely in A homogeneous region, and No. 2 image blocks are completely in B homogeneity
In region, and No. 3 image blocks are located between two homogeneous regions of A, B, but the central pixel point of No. 3 image blocks is located at B homogeneity
In region.If calculating the distance between 1 and 3 and the distance between 2 and 3 using D1, two apart from almost equal, there is D1 (1,3)
≈D1(2,3).This is because image block 1 is approximate with the left side of image block 3 similar, the right is dissimilar；And image block 2 and image block
3 the case where, situation was just with the former on the contrary, the right of image block 3 is approximate with image block 2 similar, and the left side is dissimilar.If according to
This distance is can not accurately to measure out image block 1 and image block 2 to the difference of image block 3, be easy to cause estimation marginal value
Inaccuracy.So in the present embodiment it is also contemplated that D2 in the gray value information of the central point in image block namely formula (2)
(i,j).The maximum value for taking the central point gray value of two image blocks to compare, if two blocks are in the same homogeneous region, D2 (i,
J) smaller；If two blocks are in two different regions, the pixel value meeting difference of two central points is very big, leads to D2 (i, j)
Also larger.Above-mentioned conclusion can be verified from Fig. 2, i.e., the central pixel point of 1 and 3 image blocks is not in the same homogeneous region, institute
With the central point grey value difference of two blocks is larger, it is possible to there is the center of a block in another different homogeneity area
Near domain, so obtained D2 (i, j) is larger.But the central pixel point of image block 2 and 3 is in identical homogeneous region, institute
Smaller with D2 (i, j), a possibility that tending to 0, is bigger.
As shown in figure 3, if image block NiOr NjHave one containing than more rich texture information, and two image blocks it
Between apart distant, two image blocks should have a smaller similitude at this time, and similitude both preferably is 0.Together
Sample is by taking emulating image as an example.
There is 1,2,3 three image block in Fig. 3, and three image blocks are located in the same region of search, image block 2 and figure
As all including edge in block 3, and the central point of three image blocks is all located at the same homogeneous region.If using D1 (i, j) and
Image block 1 is at a distance from image block 2 and image block 1 is at a distance from image block 3 for D2 (i, j) calculating, then can obtain D1 (1,2) ≈ D1
(1,3) and D2 (1,2) ≈ D2 (1,3).Due to including edge in image block 3, and range image block 1 is farther out, so we are uncommon
Image block 3 and image block 1 is hoped just to have lesser similitude.So herein further according to the spatial position of image block central point, building
A kind of space length measure formulas D3 (i, j), shown in expression formula such as formula (7).
Further, described according to the ratio distance, the central point gray value ratio and the center space of points
Distance calculates weighted value, comprising:
Using formula
Calculate the weighted value of pixel in two similitude windows, wherein described h1, h2 and h3 are control filtering journey respectively
The variable-definition for controlling filter strength is h1=h2=h3=kL σ by the variable of degree, and L is the view number of image, and k is constant, and σ is
The standard deviation of image, w (i, j) are the weighted value of pixel in two similitude windows.
The estimated value of pixel i is calculated according to the weighted value of pixel in above-mentioned two similitude window are as follows:
In order to illustrate the performance based on ratio and the multiplying property non-local mean Speckle Reduction Algorithm various aspects of space length,
It is tested for two class images, one type is composograph, and another kind of is true radar image.Airspace is used respectively
Frost Speckle Reduction Algorithm (hereinafter referred to as: Frost), anisotropy SRAD Speckle Reduction Algorithm (hereinafter referred to as:
SRAD), improvement SRAD Speckle Reduction Algorithm (hereinafter referred to as: GGSRAD) and SAR- based on Gaussian-Gamma double window
Four kinds of relevant restrainable algorithms of BM3D Speckle Reduction Algorithm and this paper algorithm carry out Experimental comparison.
One, composograph
As shown in figure 4, it can be seen from the figure that this paper algorithm whether inhibits the effect of coherent spot or edge to keep
Effect is all relatively good.
Wherein (a) is 16 view composograph original images in Fig. 4, and size is that (a) is 8 view composographs in 300 × 300, Fig. 5,
Size is 256 × 256.Fig. 4 b is the Frost algorithm process for Fig. 4 a as a result, (c) is the SRAD algorithm for Fig. 4 a in Fig. 4
Speckle reduction processing result, (d) is the GGSRAD algorithm Speckle reduction processing result for (a) in Fig. 4 in Fig. 4, in Fig. 4
It (e) is the radar-BM3D algorithm Speckle reduction processing result for being directed to (a) in Fig. 4, (f) is the application method for figure in Fig. 4
(a) Speckle reduction processing result in 4.
For composograph, the various parameter settings for inhibiting coherent speckle noise algorithm are as follows: Frost Speckle reduction
The local window scale of algorithm is 7 × 7；SRAD algorithm, GGSRAD algorithm all use 3 × 3 local windows, the number of iterations 100, step-length
It is 0.1；Search window is 21 × 21 in SAR-BM3D algorithm, and similitude window is 7 × 7, controls the variable h1=of filter strength
H2=h3=kL σ, k=3.
Two, true picture
As shown in figure 5, it can be seen from the figure that the ratio that inhibits in terms of Speckle reduction of this paper algorithm more thoroughly, meanwhile,
Edge does not also occur fuzzy or excess smoothness phenomenon.
Wherein, (a) is the 5 true radar images of view in Fig. 5, and image size is all 256 × 256.(b) is for Fig. 5 in Fig. 5
In (a) Frost algorithm Speckle reduction processing result, (c) is the SRAD algorithm Speckle reduction for (a) in Fig. 5 in Fig. 5
Processing result, (d) is the GGSRAD algorithm Speckle reduction processing result for (a) in Fig. 5 in Fig. 5, and (e) is to be directed in Fig. 5
Radar-BM3D algorithm Speckle reduction the processing result of (a) in Fig. 5, (f) is the application method for (a) phase in Fig. 5 in Fig. 5
Dry spot inhibits processing result.
For true radar image, the various parameter settings for inhibiting coherent speckle noise algorithm are as follows: Frost coherent spot
The local window scale of restrainable algorithms is 7 × 7；SRAD algorithm, GGSRAD algorithm all use 3 × 3 local windows, the number of iterations 80,
Step-length is 0.1；Parameter used in radar-BM3D algorithm is default value in bibliography；The search window of this paper algorithm is
21 × 21, similitude window is 7 × 7, controls variable the h1=h2=h3=kL σ, k=5 of filter strength.
(V is abbreviated as using equivalent number hereinENL) with the edge conservation degree based on ratio of averages (be abbreviated as VEPD_ROA) two
A performance indicator carries out suppression spot Performance Evaluation to the image that composograph and true radar image contain coherent spot multiplicative noise.
It obtains all kinds of algorithms shown in table 1 and table 2 and presses down spot effect performance comparison result.Wherein, the V of calculatingENLValue is each image of selection
In two pieces of homogeneous regions, respectively region 1 and region 2, calculated VENLValue is bigger, and it is strong just to represent removal noise immune；
VEPD_ROAIn HD and VD respectively represent in selected areas V calculated both horizontally and verticallyEPD_ROAValue, the two
It is strong more to represent edge holding capacity closer to 1 for value.Table 1 is for composograph, this paper algorithm and comparison algorithm performance ratio
Compared with.
Table 1
First is classified as Image Name and comparison algorithm and this paper algorithm title for radar image in table 1, and second is classified as institute
The equivalent number value for selecting two regions, is denoted as VENL, third is classified as edge holding capacity, is denoted as VEPD_ROA.Due to the ratio of this paper
Distance is suitable for Multiplicative noise model, and also adds the constraint of the gray value information and spatial positional information of central point, makes
It is whether all relatively good in terms of edge holding or in terms of Speckle reduction to obtain image.For composograph, herein
The ENL value of algorithm is more much higher than original classic algorithm.Other than radar-BM3D algorithm, the edge of this paper keeps index
Also it is higher than other algorithms.From the point of view of synthetic image and performance indicator, holding while this paper algorithm can realize inhibition coherent spot
Edge.Table 2 is for true radar image, and this paper algorithm is compared with comparing algorithm performance.
Table 2
First is classified as input picture title and each Speckle Reduction Algorithm title in table 2, and second is classified as in selected areas
Equivalent number (the V of calculatingENL), represent the ability that Speckle Reduction Algorithm inhibits noise；The edge that third is classified as calculating keeps energy
Power (VEPD_ROA), represent the marginal texture holding degree after filtering.Since this paper algorithm is by ratio distance proposed in this paper
It replaces original Euclidean distance and calculates weight, which can robustly be applied in Multiplicative noise model.Meanwhile in order to
Protect edge information information, this paper algorithm have done corresponding processing for the structure of edge.So whether directly observing image also
It is to compare performance indicator, this paper algorithm generally will be better than other comparison algorithms.
The parameter of manual setting is needed to have control filter strength variable h1, h2, h3 herein.Due to for a kind of image
Each algorithm in parameter h1, h2, h3 be identical numerical value.So next this section discusses the parameter to radar image
The influence of performance.It is analyzed for composograph and true radar image, wherein { 0.1,1,3,5,7,9,11,15 } h1=,
The value of ENL is by calculating two pieces of homogeneous regions selected in image.
As shown in Figure 6 and Figure 7, being gradually increased with parameter h1, whether to composograph or true radar image
ENL value be all gradually increased, after to certain numerical value, the rate of increase becomes slowly, and EPD_ROA value is then gradually to subtract
It is small.Thus illustrating, h1 is bigger, and Speckle reduction ability is strong, but edge holding capacity is weaker, conversely, h1 is smaller, coherent spot suppression
Ability processed is weak, and edge keeps effect fine.So one suitable h1 of selection is vital.
The present invention can guarantee the same and similar property between image and noise-free picture pixel polluted by noise.Meanwhile drawing
The gray value information of the central point of image block and the spatial positional information of central point are entered, while being able to maintain in processing image block
When including a large amount of texture information in proximal edge or image block, Protect edge information and texture information.So this paper algorithm is in phase
Dry spot maintains edge while inhibition well.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (4)
1. a kind of non-local mean radar imaging coherent spot suppressing method based on ratio distance characterized by comprising
Read synthetic aperture radar coherent spot image；
Corresponding search box size is set, corresponding similitude window size is set；
Any two similitude window ratio distance is determined according to the gray value in any two similitude window；
Any two similitude window center point is determined according to the gray value of the central point of any two similitude window
Gray value ratio；
Any two similitude window center point is determined according to the space coordinate of the central point of any two similitude window
Space length；
Two phases are calculated according to the space length of the ratio distance, the gray value ratio of the central point and the central point
Like the weighted value of pixel in property window；
The estimated value of current pixel point is estimated by the weighted value；
Any two similitude window is determined according to the gray value in similitude window size and any two similitude window
Mouth ratio distance, comprising:
Using formula
Calculate two image block ratio distances, wherein the W is the size of similitude window, wherein NiWith NjIt is respectively
With pixel QiWith QjCentered on image block, andWithIt is then two image blocks in observed image f, D1 (i, j) is two
A image block ratio distance, (i, j) are respectively the position coordinates of any two image block central pixel point, and M is any image block
Interior pixel number.
2. the method according to claim 1, wherein according to the central point of any two similitude window
Gray value determines the gray value ratio of central point between any two similitude window, comprising:
Using formula
Calculate the gray value ratio between two central pixel points, whereinIt is image block NiCentral pixel point position
The observed result of (x, y),It is image block NjCentral pixel point position (p, q) observed result, D2 (i, j) be two
Gray value ratio between a central pixel point.
3. the method according to claim 1, wherein according to the central point of any two similitude window
Space coordinate determines the space length of any two similitude window center point, comprising:
Using formula
D3 (i, j)=(x-p)2+(y-q)2 (3)
Determine the space length of any two similitude window center point, wherein (x, y) and (p, q) is two similitudes respectively
The center position coordinate of window, D3 (i, j) are the space length of two similitude window center points.
4. the method according to claim 1, wherein according to the ratio distance, the gray value of the central point
The space length of ratio and the central point calculates the weighted value of pixel in two similitude windows, comprising:
Using formula
Calculate the weighted value of pixel in two similitude windows, wherein (i, j) is respectively any two image block center pixel
The position coordinates of point, D1 are the ratio distance between two similitude windows, and D2 is central point between two similitude windows
Gray value ratio, D3 are the space length of two similitude window center points, and described h1, h2 and h3 are control filter strength respectively
Variable, be h1=h2=h3=kL σ by the variable-definition for controlling filter strength, L is the view number of image, and k is constant, and σ is figure
The standard deviation of picture, w (i, j) are the weighted value of pixel in two similitude windows.
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