CN112927165A - SAR image speckle suppression method based on NSST domain three-dimensional block matching - Google Patents
SAR image speckle suppression method based on NSST domain three-dimensional block matching Download PDFInfo
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Abstract
The invention relates to an SAR image speckle suppression method based on NSST domain three-dimensional block matching, and belongs to the technical field of radar signal processing. The method is based on a BM3D denoising framework, uses a gray scale theory to carry out similarity measurement, and carries out twice denoising and filtering on the speckle in an NSST domain, and specifically comprises the following steps: matching the gray similar blocks, and filtering and aggregating the NSST domain coherent spots; and in the second denoising, performing combined wiener filtering by using the original image and the basic estimation result to output a final speckle suppression SAR image. The method simultaneously utilizes the autocorrelation and sparsity of the SAR image, not only utilizes a gray level similar block matching method to increase the robustness of similar block matching, but also filters coherent spots in an NSST (non-subsampled transform) domain with optimal sparsity, and can remove the coherent spots and simultaneously reserve the edge texture information of the image as much as possible.
Description
Technical Field
The invention belongs to the technical field of radar signal processing, and relates to an SAR image speckle suppression method based on NSST domain three-dimensional block matching.
Background
A satellite-borne Synthetic Aperture Radar (SAR) is used as an all-weather high-resolution imaging sensor all day long and can observe the ground in real time. The great advantages of high resolution, short observation period, strong timeliness and the like are reasons for the SAR system to be widely applied by the military and the civilian. However, it has an inevitable principle disadvantage that due to the coherent imaging system inherent to the SAR, the radar echo signal has noise, so that the object appears granular on the pixels of the image, and appears as some spots with different gray levels on the SAR image, which are called coherent spot noise. The existence of speckle noise reduces the signal-to-noise ratio of the image, and brings great difficulty to SAR image segmentation, ship detection, identification and classification, so that the speckle suppression needs to be researched. The speckle suppression method can be classified into a multiview processing method before imaging and a filtering-type method after imaging, according to the time when the speckle suppression occurs. The multi-view processing technique is relatively simple, and the filtering techniques include spatial filtering, transform domain filtering, non-local mean filtering, and other methods. The spatial filtering is a static local filtering method, so that blocking effect is easily caused, and coherent speckle suppression at the detail edge is not facilitated. Transform-domain filtering can take advantage of the sparsity of images, and is a very excellent speckle suppression method. The non-local mean filtering is developed on spatial filtering, can utilize self-similarity of images, is a very promising method, and still has certain defects. Patent application publication No. CN111783583A discloses a method for suppressing SAR image speckle based on non-local mean method, which only uses self-similarity of images, but does not use sparsity of images. In recent years, researchers have proposed three-dimensional Block Matching (BM 3D) filtering by combining transform domain filtering and non-local mean filtering, and have made use of not only self-similarity but also sparsity of images, and thus have been effective in natural images. Patent application CN109919870A discloses a method for suppressing speckle in SAR images based on BM3D, in which speckle is homomorphic BM3D filtered by logarithmic transformation, but distortion occurs to some extent.
According to the method, BM3D is introduced into SAR image speckle suppression, and speckle noise is converted into additive noise by using mathematical equivalent deformation. The gray scale theory is used for measuring the similar blocks in the similar block matching, the robustness is good, and in addition, the similar block groups are transformed to a Non-sampled shear wave Transform (NSST) domain with optimal sparsity for actual coherent spot filtering. The method can effectively inhibit coherent spots and ensure the detail edge of the image.
Disclosure of Invention
In view of this, the present invention aims to provide an SAR image speckle suppression method based on three-dimensional block matching in an NSST domain, which solves the problem that the conventional SAR image speckle suppression method cannot suppress speckle and well maintain edge texture. The method introduces BM3D into SAR image speckle suppression, and converts speckle noise into additive noise by using mathematical equivalent deformation; the method has the advantages that the gray scale theory is used for measuring the similar blocks in the similar block matching, the robustness is good, and in addition, the similar block groups are transformed to a Non-sampled shear wave Transform (NSST) domain with optimal sparsity for actual coherent spot filtering; the method can effectively inhibit coherent spots and ensure the detail edge of the image.
In order to achieve the purpose, the invention provides the following technical scheme:
a SAR image speckle suppression method based on NSST domain three-dimensional block matching is based on a BM3D denoising framework, similarity measurement is carried out by using a gray scale theory, and speckle is denoised and filtered twice in the NSST domain, and the method specifically comprises the following steps: matching the gray similar blocks, and filtering and aggregating the NSST domain coherent spots; and in the second denoising, performing combined wiener filtering by using the original image and the basic estimation result to output a final speckle suppression SAR image.
Further, the method specifically comprises the following steps:
s1: inputting an SAR image to be processed, and setting a search box and a block size;
s2: calculating the similarity between all image blocks in the search box and the reference block;
s3: selecting the first Q-1 most similar image blocks, and forming a similar block group with the reference block;
s4: obtaining multi-scale and multidirectional NSST coefficients by using NSST transformation on the similar block groups, and obtaining corresponding contraction coefficients by using hard threshold filtering;
s5: performing NSST inverse transformation on the processed shrinkage coefficient, and calculating according to the similarity in the step S2 to obtain a relatively clean denoising reference block;
s6: after traversing the whole SAR image, aggregating all the de-noised image blocks according to positions to output a basic estimation SAR image;
s7: repeating the step S2 on the SAR image obtained by basic estimation to obtain a similar block group; at the moment, two similar block groups are provided, one is from the basic estimated SAR image, and the other is from the original SAR image, but the position is determined by the position of the similar block group of the basic estimated SAR image;
s8: performing combined wiener filtering and NSST inverse transformation on the two similar block groups in the NSST domain to obtain a contraction coefficient;
s9: aggregating according to the positions to obtain a final estimated SAR image;
s10: moving to the next reference window, repeating the steps S2-S9 until the whole SAR image is traversed.
Further, in step S1, inputting an SAR image to be processed, setting the size of a search box to be N × N, and setting the size of an image block to be M × M;
further, in step S2, the similarity calculation formula is:
Bi＝{Bi(1),Bi(2),……,Bi(M2-1),Bi(M2)}
Bj＝{Bj(1),Bj(2),……,Bj(M2-1),Bj(M2)}
wherein, BiAnd BjRespectively reference block and matching block, ξij(k) Is BiAnd BjThe correlation coefficient of (2).
Further, in step S2, BiAnd BjIs associated withij(k) The calculation formula of (2) is as follows:
wherein, Deltaij(k) Representing image blocks BiAnd BjAbsolute value of gray scale at kth pixel point, i.e. Δij(k)＝|Bi(k)-Bj(k)|，
The minimum difference of the image blocks is represented,
representing the maximum difference of the image blocks, m representing the mth pixel in the image block; λ denotes resolution, 0 < λ < 1, and λ is usually 0.5.
Further, in step S4, the speckle suppression process of the reference block in the NSST domain is as follows:
1) NSST conversion is carried out to obtain non-down sampling shear wave coefficients in all directions of all scales;
2) carrying out hard threshold filtering on the transformation coefficient to obtain a contracted NSST coefficient;
3) NSST inverse transformation;
4) and obtaining a reference block of the speckle suppression basic estimation according to the similarity serving as weight.
Further, in step S8, the speckle suppression process of the reference block in the NSST domain is as follows:
1) NSST conversion is carried out to obtain non-down sampling shear wave coefficients in all directions of all scales;
2) carrying out joint wiener filtering on the transformation coefficient to obtain a contracted NSST coefficient;
3) NSST inverse transformation;
4) and obtaining a reference block finally estimated by speckle suppression according to the similarity as weight.
The invention has the beneficial effects that: the method simultaneously utilizes the autocorrelation and sparsity of the SAR image, not only utilizes a gray level similar block matching method to increase the robustness of similar block matching, but also filters coherent spots in an NSST (non-subsampled transform) domain with optimal sparsity, and can remove the coherent spots and simultaneously reserve the edge texture information of the image as much as possible.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a system block diagram of an SAR image speckle suppression method based on NSST domain three-dimensional block matching according to the present invention;
fig. 2 is a comparison graph of the processing effect of the method of the present invention and other various speckle suppression methods on a real SAR image.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1 to 2, the present invention preferably selects an SAR image speckle suppression method based on three-dimensional block matching in the NSST domain, as shown in fig. 1, and keeps detailed information of a ground object target while suppressing speckle by using self-similarity and sparsity of the SAR image. The method comprises the following specific steps:
the method comprises the following steps: inputting an SAR image to be processed, setting the size of a search box to be NxN and the size of a block to be MxM;
step two: calculating the similarity between all blocks in the search box and the reference block;
first, the correlation coefficients of the two are calculated, and the calculation formula is as follows:
wherein, Deltaij(k) Representing image blocks BiAnd BjAbsolute value of gray scale at kth pixel point, i.e. Δij(k)＝|Bi(k)-Bj(k)|，
The minimum difference of the image blocks is represented,
representing the maximum difference of the image blocks, m representing the mth pixel in the image block; λ (0 < λ < 1) represents resolution, and λ is usually 0.5.
The similarity calculation formula is as follows:
wherein, Bi＝{Bi(1),Bi(2),......,Bi(M2-1),Bi(M2) And Bj＝{Bj(1),Bj(2),......,Bj(M2-1),Bj(M2) Xi reference and matching blocks xiij(k) For their correlation coefficient, k ∈ [1, M)2],k∈Z。
Step three: selecting the first Q-1 most similar image blocks, and forming a similar block group with the reference block;
step four: obtaining multi-scale and multidirectional NSST coefficients by using NSST transformation on the similar block groups, and obtaining corresponding contraction coefficients by using hard threshold filtering;
1) NSST conversion is carried out to obtain non-down sampling shear wave coefficients in all directions of all scales;
2) carrying out hard threshold filtering on the transformation coefficient to obtain a contracted NSST coefficient;
3) NSST inverse transformation;
4) and obtaining a reference block of the speckle suppression basic estimation according to the similarity serving as weight.
Step five: performing NSST inverse transformation on the processed transformation coefficient, and calculating according to the similar weight in the step S2 to obtain a relatively clean denoising reference block;
step six: and after traversing the whole SAR image, gathering all the de-noised image blocks according to positions and outputting a basic estimation SAR image.
Step seven: and repeating the second step on the SAR image obtained by the basic estimation to obtain the similar block group. At the moment, two similar block groups are provided, one is from the basic estimated SAR image, and the other is from the original SAR image, but the position is determined by the position of the similar block group of the basic estimated SAR image;
step eight: performing combined wiener filtering and NSST inverse transformation on the two similar block groups in the NSST domain to obtain a shrinkage coefficient;
1) NSST conversion is carried out to obtain non-down sampling shear wave coefficients in all directions of all scales;
2) carrying out joint wiener filtering on the transformation coefficient to obtain a contracted NSST coefficient;
3) NSST inverse transformation;
4) and obtaining a reference block finally estimated by speckle suppression according to the similarity as weight.
Step nine: and aggregating according to the position to obtain a final estimated SAR image.
The effect of the present invention is further verified and explained by the following simulation experiment.
Step ten: and moving to the next reference window, and repeating the steps from two to nine until the whole SAR image is traversed.
Comparison and verification experiment:
the picture used in the experiment is Kilauea (512 × 512), as shown in FIG. 2(a), and the method of the present invention is compared with the SAR-BM3D with good classical enhanced Lee filtering and denoising effects. As can be seen from fig. 2, the effect of using the enhanced Lee filter is the worst, which causes excessive blurring of the image and does not suppress the coherent spots well, as in fig. 2 (b); while SAR-BM3D suppresses speckle noise well, it also produces too much smoothing on the image, and does not preserve the detail edges of the image well, as in fig. 2 (c); the image processed by the method of the present invention (as shown in fig. 2(d)) avoids the above disadvantages, and the image edge is well maintained and the coherent spots are suppressed.
In conclusion, the effectiveness of the invention is verified by simulation experiments.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (5)
1. A SAR image speckle suppression method based on NSST domain three-dimensional block matching is characterized in that the method is based on a BM3D denoising framework, similarity measurement is carried out by using a gray scale theory, and speckle is denoised and filtered twice in an NSST domain, and the method specifically comprises the following steps: matching the gray similar blocks, and filtering and aggregating the NSST domain coherent spots; in the second denoising, performing combined wiener filtering by using the original image and the basic estimation result to output a final speckle suppression SAR image; here, BM3D is three-dimensional Block Matching (Block Matching and 3D, BM3D), and NSST is Non-sampled shear wave Transform (NSST).
2. The SAR image speckle suppression method according to claim 1, characterized by specifically comprising the steps of:
s1: inputting an SAR image to be processed, and setting a search box and a block size;
s2: calculating the similarity between all image blocks in the search box and the reference block;
s3: selecting the first Q-1 most similar image blocks, and forming a similar block group with the reference block;
s4: obtaining multi-scale and multidirectional NSST coefficients by using NSST transformation on the similar block groups, and obtaining corresponding contraction coefficients by using hard threshold filtering;
s5: performing NSST inverse transformation on the processed shrinkage coefficient, and calculating according to the similarity in the step S2 to obtain a relatively clean denoising reference block;
s6: after traversing the whole SAR image, aggregating all the de-noised image blocks according to positions to output a basic estimation SAR image;
s7: repeating the step S2 on the SAR image obtained by basic estimation to obtain a similar block group; at the moment, two similar block groups are provided, one is from the basic estimated SAR image, and the other is from the original SAR image, but the position is determined by the position of the similar block group of the basic estimated SAR image;
s8: performing combined wiener filtering and NSST inverse transformation on the two similar block groups in the NSST domain to obtain a contraction coefficient;
s9: aggregating according to the positions to obtain a final estimated SAR image;
s10: moving to the next reference window, repeating the steps S2-S9 until the whole SAR image is traversed.
3. The method for suppressing speckle in an SAR image according to claim 1, wherein in step S2, assuming that the size of the image block is mxm, the similarity calculation formula is:
Bi＝{Bi(1),Bi(2),......,Bi(M2-1),Bi(M2)}
Bj＝{Bj(1),Bj(2),......,Bj(M2-1),Bj(M2)}
wherein, BiAnd BjRespectively reference block and matching block, ξij(k) Is BiAnd BjThe correlation coefficient of (2).
4. The SAR image speckle suppression method according to claim 3, wherein in step S2, BiAnd BjIs associated withij(k) The calculation formula of (2) is as follows:
wherein, Deltaij(k) Representing image blocks BiAnd BjAbsolute value of gray scale at kth pixel point, i.e. Δij(k)＝|Bi(k)-Bj(k)|，
The minimum difference of the image blocks is represented,
representing the maximum difference of the image blocks, m representing the mth pixel in the image block; λ represents resolution, 0 < λ < 1.
5. The SAR image speckle suppression method according to claim 1, wherein in step S4 or S8, the speckle suppression process of the reference block in the NSST domain is as follows:
1) NSST conversion is carried out to obtain non-down sampling shear wave coefficients in all directions of all scales;
2) filtering the transform coefficients to obtain shrunk NSST coefficients; the filtering is hard threshold filtering in step S4, and joint wiener filtering in step S8;
3) NSST inverse transformation;
4) and obtaining a reference block of the speckle suppression estimation by taking the similarity as weight.
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