CN109035152B - Non-local mean filtering method for synthetic aperture radar image - Google Patents

Non-local mean filtering method for synthetic aperture radar image Download PDF

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CN109035152B
CN109035152B CN201810502002.9A CN201810502002A CN109035152B CN 109035152 B CN109035152 B CN 109035152B CN 201810502002 A CN201810502002 A CN 201810502002A CN 109035152 B CN109035152 B CN 109035152B
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CN109035152A (en
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武俊杰
童丹平
叶宏达
王井增
沙连童
王雯璟
杨海光
杨建宇
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University of Electronic Science and Technology of China
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Abstract

The invention provides a synthetic aperture radar image non-local mean filtering method, and belongs to the field of radar image processing. The invention introduces the heterogeneity measure into the non-local mean algorithm, and improves the weight measurement mode of the non-local mean algorithm. Firstly, aiming at the traditional non-local mean method, an equal weight value weighting method is adopted for calculating distance measure, a variation coefficient is introduced to calculate the weighted weight value of the distance measure of a pixel point in an image block window, and the variation coefficient can represent the texture direction of an image, so that the similarity of the image block after the introduction of the variation coefficient and weighting can more effectively capture the image block with similarity in the texture and direction, thereby avoiding the ambiguity of marginality and directivity; then, a self-adaptive degradation parameter function based on the variation coefficient is designed, the degradation parameter is adjusted in a self-adaptive mode, and the heterogeneous region can be effectively protected from being excessively smooth. Therefore, the method can more effectively keep the texture edge information while filtering out the coherent spots.

Description

Non-local mean filtering method for synthetic aperture radar image
Technical Field
The invention belongs to the field of radar image processing, and particularly relates to a synthetic aperture radar image non-local mean filtering method.
Background
Synthetic Aperture Radar (SAR) is an active high-resolution imaging sensor, can perform observation all day long and all weather, has strong penetrability, and is widely applied to the fields of military investigation, disaster monitoring, surface covering layer detection and the like. However, since the SAR imaging is performed by using echo coherent superposition, speckle noise inevitably exists in the SAR image, and the speckle noise reduces the visual effect of the SAR image, thereby causing difficulty in subsequent image segmentation and target identification. Therefore, speckle suppression of the SAR image is of great significance to segmentation, classification and identification of the SAR image.
The speckle filtering method of the SAR image can be divided into spatial filtering and transform domain filtering. The spatial filtering means that neighborhood pixels of pixels to be filtered are weighted and averaged to obtain a filtered gray value, and the methods are represented by Lee filtering, Frost filtering, Gamma-Map filtering and the like, which are provided based on a specific speckle distribution model, so that the method only has a good filtering effect on SAR images with specific distribution. Transform domain filtering refers to a method of transforming an image from a time domain to a corresponding transform domain by fourier transform or wavelet transform, then performing filtering processing on the transform domain, and finally performing inverse transform to return a time domain two-dimensional image. Both of these methods are essentially based on local or semi-local filtering methods, without taking into account the structural redundancy information of the image itself.
In order to fully utilize the self structural redundancy information of an image, a Non-Local Means (NLM) algorithm is provided, the algorithm utilizes the structural redundancy of the image, an image block similar to a target image block is searched in the image, corresponding weight is given to a pixel point according to the similarity between the image blocks, and an estimated value of the target pixel point is obtained by a weighted average method. In the prior art, a method is provided for providing a BM3D filtering method based on NLM algorithm by combining wavelet domain filtering and wiener filtering ideas, the method includes two estimations of basic estimation and final estimation, the preliminary estimation gathers similar blocks into a three-dimensional array, filtering the three-dimensional array by adopting a joint hard threshold, returning filtered image blocks to their original positions to obtain overlapped block estimation, then carrying out weighted average to obtain a preliminarily estimated image, the final estimation is to gather a noisy image and the preliminarily estimated image into two three-dimensional images respectively by utilizing block similarity, then using joint wiener filtering, putting the filtered image blocks to the original positions to obtain an overlapped estimated image, weighting to obtain a final estimated image, and obtaining a good effect. However, the above methods are only applicable to additive white gaussian noise, but not on SAR images with multiplicative noise, and therefore, many scholars have studied the application of NLM algorithm on SAR images. Therefore, a (PPB) non-local mean filtering method based on image block probability distribution is provided, the method utilizes the noise distribution characteristic of the image, maximum likelihood estimation is adopted when the block similarity is calculated, the estimation is more and more accurate in the iteration process, and the method obtains good filtering effect on the SAR image; in addition, another method proves that the ratio measure can represent the distance measure of the SAR image more stably, and provides a homogeneous region non-local mean filtering method based on the ratio measure, but the method is only suitable for SAR image denoising in the homogeneous region; on the basis of BM3D, an SAR-BM3D method is provided, the method for improving block similarity measurement is provided, the problem of wavelet coefficient shrinkage is solved, and a very good filtering effect is obtained on an SAR image; and a method is also provided, on the basis of the ratio measurement non-local mean filtering, the window weight of the distance measurement is improved, so that the ratio measurement non-local mean filtering proposed by Hongxiao Feng can be popularized to a heterogeneous region. However, when the distance measurement is calculated by these methods, the average value of the gray-scale distances of the pixels in the window or the gaussian weighted value is used, and the texture direction information of the image is not considered, but a large amount of texture direction information exists in the SAR image, and the information should be an important basis for the distance measurement. In addition, in the NLM algorithm, the weight is a gaussian kernel function depending on the distance measure, where the degradation parameter has a very important influence on the filtering effect, however, there are few documents on the research on the degradation parameter, which is generally set according to an empirical value, and the adaptive denoising effect is not achieved.
Disclosure of Invention
In order to solve the above problems, the invention provides a non-local mean filtering method for a synthetic aperture radar image, which introduces a Coefficient of Variation (CV) to construct an anisotropic two-dimensional weighting window that changes adaptively according to the image; in addition, a method for adaptively adjusting degradation parameters based on the variation coefficient is provided, so that the image has a good denoising effect in a homogeneous region, and meanwhile, edge texture details can be kept in a heterogeneous region.
A synthetic aperture radar image non-local mean filtering method comprises the following steps:
step 1, obtaining a synthetic aperture radar amplitude image and initializing parameters, wherein the initialized parameters comprise a search window size Ds, an image window size Ds, a degradation parameter coefficient gamma and an S curve gradient parameter xi;
step 2, calculating to obtain speckle distribution of the image, and obtaining noise variance of the speckle based on the speckle distribution;
step 3, calculating an image variation coefficient according to the standard deviation and the mean value of the image;
step 4, calculating self-adaptive degradation parameters according to the image variation coefficient;
step 5, constructing a self-adaptive filtering window based on the variation coefficient;
and 6, obtaining the value of the pixel point based on the non-local algorithm of the heterogeneity measure.
Further, the step 1 further includes the following steps:
setting a pixel point i as a pixel positioned at an image position i, setting an image block i as an image block with the size ds and the pixel point i as the center, and searching a region omegaiThe image blocks are image blocks with the size Ds and with the pixel point i as the center.
Further, the step 2 comprises the following steps:
when the image pixel spacing is comparable to the radar imaging resolution unit, the noise power is uncorrelated, and the speckle is incoherent multiplicative noise, denoted as
υ＝u·n
Wherein upsilon is an observed value, u is a radar Reflection Coefficient (RCS), n is speckle noise, and the equivalent visual number of the image can be obtained according to the standard deviation and the mean value of the image
Figure GDA0001844984690000041
Wherein, L is equivalent vision, sigma is standard deviation of the image, mu is mean value of the image, the coherent speckles are completely developed, and the distribution function of the intensity condition is
Figure GDA0001844984690000042
Wherein Gamma (·) is Gamma function, radar reflection coefficient is set to 1, and completely developed coherent speckle noise distribution is obtained
Figure GDA0001844984690000043
The variance of the noise intensity is calculated as
Figure GDA0001844984690000044
The amplitude value of the image is the square root of the intensity, and the amplitude condition distribution of the obtained observation value is
Figure GDA0001844984690000045
Setting the radar reflection coefficient to 1, the distribution function of amplitude noise can be obtained
Figure GDA0001844984690000046
The noise variance is calculated as
Figure GDA0001844984690000051
Further, the step 3 includes the following steps:
coefficient of variation of
Figure GDA0001844984690000052
Wherein, CV is a local variation coefficient of the pixel point.
Further, the step 4 includes the following steps:
adaptively adjusting the coefficient of variation based on an S-curve represented as
Figure GDA0001844984690000053
Wherein the parameter tau is used for controlling the offset of the S curve, and the parameter xi is used for controlling the inclination of the S curve;
according to the variation coefficient, for each pixel point in the image, the self-adaptive degradation parameter is expressed as
Figure GDA0001844984690000054
Wherein h' is an adaptive degradation factor, h ═ γ σ, CVmaxIs the maximum local variation coefficient of the entire image.
Further, the step 5 comprises the following steps:
according to the multiplicative noise model in the step 2, the noise is white Gaussian noise with the mean value as a unit value, and the variation coefficient of one image block is expressed as
Figure GDA0001844984690000055
Where Ψ is an image block in the observation image v, and E (u) if the image block is in a homogeneous regionΨ 2)＝[E(uΨ)]2The coefficient of variation can be simplified to
Figure GDA0001844984690000061
Based on the noise value as a unit value
Figure GDA0001844984690000062
The image block variation coefficient in the homogeneous region is approximate to the image noise variance, and the anisotropic filtering window constructed according to the local variation coefficient of the image is expressed as
ACV(i,k)＝exp(-|CV(i,k)-CV(i)|2)
Wherein A isCVAnd (i, k) is an anisotropic weighting window taking i as a center, and CV (i, k) is a variation coefficient of the kth pixel point in the image block taking i as a center.
Further, the step 6 includes the following steps:
step 61, calculating image block similarity measure
Based on a ratio measure and the adaptive filtering window, a similarity measure is expressed as
Figure GDA0001844984690000063
Wherein d isCV(i,j)Is the similarity measure of the image blocks with the pixel points i and j as the center, psi is the image block, ZCV(i) The function xi (x) is a normalization factor of the coefficients of variation in the image block, and has an expression xi (x) x +1/x, ui(k) Is the gray value, u, of the k-th pixel point of the image block centered at ij(k) The gray value of the kth pixel point of the image block taking j as the center;
step 62, calculating the weight of the pixel point
According to the NLM algorithm, based on the Gaussian kernel function, calculating the weight between the pixel points
Figure GDA0001844984690000064
Wherein the content of the first and second substances,
Figure GDA0001844984690000065
is a normalized coefficient;
step 63, calculating pixel values of pixel points
According to the NLM algorithm, for a pixel point i, a search area omega is formediThe pixel points in the image are overlapped according to the weight value to obtain the estimated value of the pixel point i
Figure GDA0001844984690000071
Wherein u (j) is the gray value corresponding to the pixel j.
The invention has the beneficial effects that: the invention introduces the heterogeneity measure into the non-local mean algorithm, and improves the weight measurement mode of the non-local mean algorithm. Firstly, aiming at the traditional non-local mean method, an equal weight value weighting method is adopted for calculating distance measure, a variation coefficient is introduced to calculate the weighted weight value of the distance measure of a pixel point in an image block window, and the variation coefficient can represent the texture direction of an image, so that the similarity of the image block after the introduction of the variation coefficient and weighting can more effectively capture the image block with similarity in the texture and direction, thereby avoiding the ambiguity of marginality and directivity; then, a self-adaptive degradation parameter function based on the variation coefficient is designed, the degradation parameter is adjusted in a self-adaptive mode, and the heterogeneous region can be effectively protected from being excessively smooth. Therefore, the method can more effectively keep the texture edge information while filtering out the coherent spots.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is a flowchart of step 6 in fig. 1.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, the non-local mean filtering method for synthetic aperture radar image according to the present invention is implemented by the following steps:
step 1, obtaining a synthetic aperture radar amplitude image and initializing parameters, wherein the initialized parameters comprise a search window size Ds, an image window size Ds, a degradation parameter coefficient gamma and an S curve gradient parameter xi.
In this embodiment, an input image SAR airborne amplitude map includes homogeneous region image blocks a and heterogeneous region image blocks B, the size of the image blocks is 30 × 30, the image blocks are used for image filtering result evaluation, the wave band is an X wave band, the resolution is 3 meters, and the size of the image is 256 × 256. The initialized parameters comprise a search window size Ds, an image window size Ds, a degradation parameter coefficient gamma and an S curve gradient parameter xi.
In this embodiment, a pixel point i is set as a pixel located at an image position i, an image block i is an image block with a size ds and the pixel point i as a center, and a search area ΩiThe image blocks are image blocks with the size Ds and with the pixel point i as the center.
And 2, calculating to obtain the speckle distribution of the image, and obtaining the noise variance of the speckle based on the speckle distribution.
In this embodiment, step 2 is implemented by the following process:
when the image pixel spacing is comparable to the radar imaging resolution unit, the noise power is uncorrelated, and the speckle can be considered to be incoherent multiplicative noise, denoted as
υ＝u·n
Wherein upsilon is an observed value, u is a real radar Reflection Coefficient (RCS), n is speckle noise, an equivalent view of the image can be obtained according to a standard deviation and a mean value of the input image, and an expression of the equivalent view is
Figure GDA0001844984690000081
Wherein, L is equivalent vision, sigma is standard deviation of the image, mu is mean value of the image, for SAR image with vision of L, several spots are completely developed, the intensity condition distribution function can be expressed as
Figure GDA0001844984690000082
Wherein Gamma (·) is Gamma function, radar reflection coefficient is set to 1, and completely developed coherent speckle noise distribution is obtained
Figure GDA0001844984690000083
The variance of the noise intensity is calculated as
Figure GDA0001844984690000091
The amplitude value of the SAR image is the square root of the intensity, and the amplitude condition distribution of the obtained observed value is
Figure GDA0001844984690000092
Setting the radar reflection coefficient to 1, the distribution function of amplitude noise can be obtained
Figure GDA0001844984690000093
The noise variance is calculated as
Figure GDA0001844984690000094
And 3, calculating an image variation coefficient according to the standard deviation and the mean value of the image.
In this embodiment, step 3 is implemented by the following process:
the coefficient of variation is the ratio of the standard deviation and the mean of the image, i.e.
Figure GDA0001844984690000095
For each pixel in the SAR image, the local variation coefficient of the pixel position is the variation coefficient of the pixel estimated by taking the local neighborhood image block of the pixel, the local neighborhood size of the pixel is consistent with the image block size definition, and the ds multiplied by ds image block taking the pixel as the center is taken to be used for calculating the variation coefficient of the center pixel:
Figure GDA0001844984690000096
wherein σΨAnd muΨAnd the standard deviation and the mean value of the local neighborhood of the pixel point.
And 4, calculating a self-adaptive degradation parameter according to the image variation coefficient.
In this embodiment, step 4 is implemented by the following process:
in the traditional NLM algorithm, the degradation factor of the exponential function of the weight is calculated according to the similarity measure and is constant and unchangeable in the whole image, so the selection of the degradation factor is very important, if the degradation factor is too large, the excessive smoothness is caused, the texture edge of the image is not favorably maintained, if the degradation factor is too small, a satisfactory filtering effect cannot be achieved in a homogeneous region, and because the degradation factor can distinguish the homogeneous region from a heterogeneous region, the invention provides a self-adaptive degradation factor calculation method by combining the degradation factor, so that the degradation factor can be automatically adjusted to adapt to the filtering requirements of images in different regions. The definition of the variation coefficient shows that the variation coefficient of the homogeneous region is smaller and close to the noise variance, the variation coefficient of the heterogeneous region is far larger than the noise variance, and by combining the analysis of the degradation factor, the variation coefficient is adaptively adjusted by using an S-shaped curve, and the expression of the S-shaped curve is as follows:
Figure GDA0001844984690000101
wherein, the parameter tau is used for controlling the offset of the S curve, and the parameter xi is used for controlling the inclination of the S curve.
According to the variation coefficient, for each pixel point in the image, the adaptive degradation parameter is expressed as
Figure GDA0001844984690000102
H' is an adaptive degradation factor, h is the same as a traditional non-local mean value, the value of the parameter h is related to the noise variance of the image, and is an empirical value, generally, h is taken as gamma sigma, wherein the value range of gamma is [90,100 ]]Better effect can be achieved. Since the variation coefficient of the homogeneous region is close to the noise variance and the variation coefficient of the heterogeneous region is much larger than the noise variance, the parameter τ is selected from a value calculated when the position of the symmetric point of the sigmoid curve is equal to the noise variance, that is, when CV is σ, τ + ξ (CV/CV) is equal to σmax) Solving for tau-xi (CV/CV) by 0max) The parameter xi is used for controlling the inclination of the S-shaped curve, and the range of the obtained parameter xi is [20,50 ] through a large number of experiments]It is noted that this step is calculated for the global variation coefficient of the entire image, where the parameter represents the global maximum variation coefficient of the image. CV ofmaxThe maximum local variation coefficient of the whole image is used for normalizing the variation coefficient.
And 5, constructing a self-adaptive filtering window based on the variation coefficient.
In this embodiment, step 5 is implemented by the following process:
according to the step 2, it can be known that speckle noise of the SAR image can be simplified into multiplicative noise, and if the noise is white gaussian noise with a mean value of unity, according to the multiplicative noise model of the step B, the variation coefficient of one image block can be represented as
Figure GDA0001844984690000111
Where Ψ is an image block in the observation image v, and E (u) if the image block is in a homogeneous regionΨ 2)＝[E(uΨ)]2The coefficient of variation can be simplified to
Figure GDA0001844984690000112
Since the noise mean value is unity, it can be obtained
Figure GDA0001844984690000113
That is, therefore, the image block variation coefficients in the homogeneous region can be approximated as the image noise variance; if the image block is in a heterogeneous area, E (u)Ψ 2)＞＞[E(uΨ)]2Therefore, the image block variation coefficient in the heterogeneous region is far larger than the image noise variance, that is, the value of the variation coefficient at the edge texture is far larger than that of the homogeneous region, and by utilizing the characteristic of the local variation coefficient of the image, a self-adaptive anisotropic two-dimensional filtering window can be constructed, the anisotropic filtering window is introduced into the distance measurement, so that the precision of the similarity measurement is increased, the pixel points with high similarity can be selected more effectively, and the edge texture target and the point target can be protected more effectively; meanwhile, in the homogeneous region, because the variation coefficient is approximate to the noise variance, and the filtering window is approximate to flat, isotropic filtering can be ensured, namely, under the condition that the homogeneous region is not influenced, the edge texture direction information is fully utilized for weighting, and the direction details of the image are effectively captured. The anisotropic filtering window constructed from the image local variation coefficients can be expressed as:
ACV(i,k)＝exp(-|CV(i,k)-CV(i)|2)
wherein A isCVAnd (i, k) is an anisotropic weighting window taking i as a center, and CV (i, k) is a variation coefficient of the kth pixel point in the image block taking i as a center. If i is located on the texture, the edge pixel can be assigned with high weight value by using the variation coefficient, and the pixel point irrelevant to the edge direction is assigned with low weight value, thereby achieving the purpose of direction selection, then ACV(i, k) forming a filtering window along the image texture; if i is located on the point target, the variation coefficient of the point target is far larger than that of the surrounding pixel points, so that a central weighted form can be obtained, and ACV(i, k) the distribution resembles a gaussian filter window; if i is in the homogeneous region, since the variation coefficients of the homogeneous region are close to the noise variance, each pixel point is almost endowed with the same weight, and A isCV(i, k) is a flat filter window with uniform distribution.
And 6, obtaining the value of the pixel point based on the non-local algorithm of the heterogeneity measure.
Referring to fig. 2, step 6 is implemented by the following process:
step 61, calculating image block similarity measure
According to the characteristic that coherent speckles can be modeled into multiplicative noise in a simplified mode, the similarity measurement is calculated by using the ratio measurement, and in combination with the self-adaptive filtering window constructed in the step 5, for image blocks taking i and j as centers in a search area, the similarity measurement is expressed as
Figure GDA0001844984690000121
Wherein d isCV(i, j) is the similarity measure of the image blocks with the pixel points i and j as the center, Ψ is the image block,
Figure GDA0001844984690000122
the function xi (x) is a normalization factor of the coefficients of variation in the image block, and is expressed by xi (x) x +1/x, r (i, j) ui(k)/uj(k) The gray scale of the kth pixel point of the image block taking i and j as the centerValue ui(k) And uj(k) The ratio between r (i, j) and r (i, j) is shown to be closer to 1, indicating that the two gray values are closer together, and further away from 1, indicating that the two gray values differ more, and that the function xi () is a function [0, + ∞ ]]The function is decremented and then incremented and the minimum is taken when r (i, j) equals 1.
Step 62, calculating the weight of the pixel point
According to the NLM algorithm, a Gaussian kernel function is used for calculating the weight of the pixel points, the similarity measure of the image blocks is used as a distance, the self-adaptive degradation coefficient calculated in the step 4 is used as the degradation coefficient, normalization is carried out, and the weight among the pixel points can be expressed as
Figure GDA0001844984690000123
Wherein the content of the first and second substances,
Figure GDA0001844984690000131
for the normalization coefficient, h' is an adaptive degradation factor for controlling the decay rate of the exponential function.
Step 63, calculating pixel values of pixel points
According to the NLM algorithm, for a pixel point i, a search area omega is formediThe pixel points in the image are overlapped according to the weight value to obtain the estimated value of the pixel point i
Figure GDA0001844984690000132
Wherein u (j) is the gray value corresponding to the pixel j.
The invention estimates the pixel value of each pixel point as NLCV[u](i) In that respect The method can effectively keep the texture edge information while filtering out the coherent spots.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (5)

1. A non-local mean filtering method for a synthetic aperture radar image is characterized by comprising the following steps:
step 1, obtaining a synthetic aperture radar amplitude image and initializing parameters, wherein the initialized parameters comprise a search window size Ds, an image window size Ds, a degradation parameter coefficient gamma and an S curve gradient parameter xi;
step 2, calculating to obtain speckle distribution of the image, and obtaining noise variance of the speckle based on the speckle distribution;
step 3, calculating an image variation coefficient according to the standard deviation and the mean value of the image;
step 4, calculating self-adaptive degradation parameters according to the image variation coefficient; the method comprises the following steps:
adaptively adjusting the coefficient of variation based on an S-curve represented as
Figure FDA0003448964580000011
Wherein the parameter tau is used for controlling the offset of the S curve, and the parameter xi is used for controlling the inclination of the S curve;
according to the variation coefficient, for each pixel point in the image, the self-adaptive degradation parameter is expressed as
Figure FDA0003448964580000012
Wherein h' is an adaptive degradation factor, h ═ γ σ, CVmaxThe maximum local variation coefficient of the whole image;
step 5, constructing a self-adaptive filtering window based on the variation coefficient; the method comprises the following steps:
according to the multiplicative noise model in the step 2, the noise is white Gaussian noise with the mean value as a unit value, and the variation coefficient of one image block is expressed as
Figure FDA0003448964580000013
Where Ψ is an image block in the observation image v, and E (u) if the image block is in a homogeneous regionΨ 2)＝[E(uΨ)]2And the coefficient of variation is simplified to
Figure FDA0003448964580000014
Based on the noise value as a unit value
Figure FDA0003448964580000015
The image block variation coefficient in the homogeneous region is the image noise variance, and the anisotropic filtering window constructed based on the local variation coefficient of the image is expressed as
ACV(i,k)＝exp(-|CV(i,k)-CV(i)|2)
Wherein A isCV(i, k) is an anisotropic weighting window taking i as a center, and CV (i, k) is a variation coefficient of a kth pixel point in an image block taking i as a center;
and 6, obtaining the value of the pixel point based on the similarity measure of the image block.
2. The non-local mean filtering method for synthetic aperture radar images according to claim 1, wherein the step 1 further comprises the following steps:
setting a pixel point i as a pixel positioned at an image position i, setting an image block i as an image block with the size ds and the pixel point i as the center, and searching a region omegaiThe image blocks are image blocks with the size Ds and with the pixel point i as the center.
3. The non-local mean filtering method for synthetic aperture radar images according to claim 2, wherein the step 2 comprises the following steps:
when the image pixel spacing and the radar imaging resolution unit are the same, the noise power is uncorrelated, and the coherent speckle is incoherent multiplicative noise expressed as
υ＝u·n
Wherein upsilon is an observed value, u is a radar Reflection Coefficient (RCS), n is speckle noise, and the equivalent visual number of the image can be obtained according to the standard deviation and the mean value of the image
Figure FDA0003448964580000021
Wherein, L is equivalent vision, sigma is standard deviation of the image, mu is mean value of the image, the coherent speckles are completely developed, and the distribution function of the intensity condition is
Figure FDA0003448964580000022
Wherein Gamma (·) is Gamma function, radar reflection coefficient is set to 1, and completely developed coherent speckle noise distribution is obtained
Figure FDA0003448964580000023
The variance of the noise intensity is calculated as
Figure FDA0003448964580000024
The amplitude value of the image is the square root of the intensity, and the amplitude condition distribution of the obtained observation value is
Figure FDA0003448964580000025
Setting the radar reflection coefficient to 1, the distribution function of amplitude noise can be obtained
Figure FDA0003448964580000026
The noise variance is calculated as
Figure FDA0003448964580000031
4. The non-local mean filtering method for synthetic aperture radar images according to claim 3, wherein the step 3 comprises the following steps:
coefficient of variation of
Figure FDA0003448964580000032
Wherein, CV is a local variation coefficient of the pixel point.
5. The non-local mean filtering method for synthetic aperture radar images according to claim 1, wherein the step 6 comprises the following steps:
step 61, calculating image block similarity measure
Based on a ratio measure and the adaptive filtering window, a similarity measure is expressed as
Figure FDA0003448964580000033
Wherein d isCV(i, j) is the similarity measure of the image blocks with the pixel points i and j as the center, psi is the image block, ZCV(i) The function xi (x) is a normalization factor of the coefficients of variation in the image block, and has an expression xi (x) x +1/x, ui(k) The k-th pixel point of the image block taking i as the centerGray value of uj(k) The gray value of the kth pixel point of the image block taking j as the center;
step 62, calculating the weight of the pixel point
According to the NLM algorithm, based on the Gaussian kernel function, calculating the weight between the pixel points
Figure FDA0003448964580000034
Wherein the content of the first and second substances,
Figure FDA0003448964580000035
is a normalized coefficient;
step 63, calculating pixel values of pixel points
According to the NLM algorithm, for a pixel point i, a search area omega is formediThe pixel points in the image are overlapped according to the weight value to obtain the estimated value of the pixel point i
Figure FDA0003448964580000036
Wherein u (j) is the gray value corresponding to the pixel j.
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