SAR image speckle noise suppression method based on sub-aperture decomposition

Abstract

The invention discloses an SAR image speckle noise suppression method based on sub-aperture decomposition, and belongs to the field of radar image processing. The method comprises the following steps: respectively carrying out sub-aperture segmentation processing and modulus quantization processing on single-view complex image data of an SAR image to sequentially obtain a sub-aperture image and a full-aperture image of the single-view complex image data; performing logarithmic transformation on an original three-dimensional tensor formed by the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor; and according to the noisy three-dimensional tensor, establishing a noise reduction objective function model containing a global low-rank regular term, a non-local denoising regular term and an edge preserving regular term, carrying out iterative updating on the noise reduction objective function model until a preset maximum number of iterations is met, and outputting to obtain a final noise reduction image for suppressing speckle noise. According to the invention, the imaging quality of the SAR image after noise reduction processing can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar image processing, and in particular to a SAR image speckle noise suppression method based on sub-aperture decomposition. Background Art

[0002] Synthetic aperture radar (SAR), as an important active microwave imaging sensor, plays a key role in numerous fields, including environmental monitoring, geological mapping, and ocean observation, thanks to its all-day, all-weather operation. However, due to SAR's unique coherent imaging mechanism, its images are inevitably affected by speckle noise. Speckle noise is a multiplicative noise, fundamentally different from the Gaussian additive noise in optical images. It severely degrades the quality of SAR images, significantly reducing their interpretability and significantly hindering subsequent tasks such as target detection, recognition, and classification.

[0003] In the related art, although existing processing methods have achieved certain results in suppressing speckle noise in SAR images, these methods fail to fully explore and utilize the auxiliary noise reduction function of sub-aperture images generated by SAR images, which results in low quality of SAR images generated when suppressing speckle noise.

[0004] Based on this, there is an urgent need for a SAR image speckle noise suppression method based on sub-aperture decomposition to solve the above technical problems. Summary of the Invention

[0005] The present invention provides a method for suppressing speckle noise in SAR images based on subaperture decomposition, which can improve the imaging quality of SAR images that have undergone speckle noise suppression. The technical solution is as follows:

[0006] In one aspect, a method for suppressing speckle noise in SAR images based on subaperture decomposition is provided, the method comprising:

[0007] performing sub-aperture segmentation processing and modulus quantization processing on the single-view complex image data of the SAR image, respectively, to obtain a sub-aperture image and a full-aperture image of the single-view complex image data in sequence;

[0008] Performing logarithmic transformation on an original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor;

[0009] A denoising objective function model including a global low-rank regularization term, a non-local denoising regularization term, and an edge-preserving regularization term is established according to the noisy three-dimensional tensor. The denoising objective function model is iteratively updated until a preset maximum number of iterations is met, and a final denoised image with suppressed speckle noise is output.

[0010] On the other hand, a device for suppressing speckle noise in SAR images based on subaperture decomposition is provided, the device comprising:

[0011] a processing module, configured to perform sub-aperture segmentation processing and modulus quantization processing on the single-view complex image data of the SAR image, and sequentially obtain a sub-aperture image and a full-aperture image of the single-view complex image data;

[0012] a generating module, performing logarithmic transformation on an original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor;

[0013] An updating module is configured to establish, based on the noisy three-dimensional tensor, a denoising objective function model including a global low-rank regularization term, a nonlocal denoising regularization term, and an edge-preserving regularization term, and iteratively update the denoising objective function model until a preset maximum number of iterations is met, thereby outputting a final denoised image with suppressed speckle noise.

[0014] In another aspect, a computer device is provided, comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to implement the steps of the above-described method for suppressing SAR image speckle noise based on subaperture decomposition.

[0015] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for suppressing SAR image speckle noise based on subaperture decomposition are implemented.

[0016] On the other hand, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for suppressing SAR image speckle noise based on subaperture decomposition.

[0017] The technical solution provided by this invention can achieve at least the following beneficial effects: First, the single-view complex image data of a SAR image is subjected to sub-aperture segmentation to obtain multiple sub-aperture images. A full-aperture image is obtained through modulo and quantization. The full-aperture image and multiple sub-aperture images are stacked into a set of three-dimensional tensors. Based on a preset denoising objective function model, the transformed three-dimensional tensor is then iteratively updated, including global low-rank regularization, non-local denoising regularization, and edge-preserving regularization, to obtain the final denoised image. This method not only effectively suppresses speckle noise but also better preserves image detail features, effectively improving SAR image quality, thereby effectively promoting the in-depth application and continued development of SAR technology in various fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of a method for suppressing speckle noise in SAR images based on subaperture decomposition according to an embodiment of the present invention;

[0020] Figure 2 is a flow chart of a subaperture decomposition method provided by one embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of Hamming window truncation provided by an embodiment of the present invention;

[0022] Figure 4 This is a SAR image with obvious speckle noise provided by an embodiment of the present invention;

[0023] Figure 5 is a SAR image after noise reduction provided by an embodiment of the present invention;

[0024] Figure 6 This is a structural diagram of a SAR image speckle noise suppression device based on subaperture decomposition provided by one embodiment of the present invention;

[0025] Figure 7 This is a hardware architecture diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] As mentioned above, although existing speckle suppression methods can achieve certain results, these methods still have various limitations and fail to fully exploit the imaging characteristics of SAR images.

[0028] Based on this, the concept of the present invention is to use the sub-aperture image obtained by sub-aperture decomposition as additional information to assist the speckle noise suppression task of SAR images. This not only can effectively suppress speckle noise, but also can preserve the image detail features, thereby effectively improving the SAR image quality.

[0029] The specific implementation of the above concept is described below.

[0030] Please refer to Figure 1 An embodiment of the present invention provides a method for suppressing speckle noise in SAR images based on subaperture decomposition, the method comprising:

[0031] Step 100, performing sub-aperture segmentation processing and modulus quantization processing on the single-view complex image data of the SAR image, to obtain a sub-aperture image and a full-aperture image of the single-view complex image data in sequence;

[0032] Step 102, performing logarithmic transformation on the original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor;

[0033] Step 104: Establish a denoising objective function model including a global low-rank regularization term, a non-local denoising regularization term, and an edge-preserving regularization term based on the noisy three-dimensional tensor. It iteratively update the denoising objective function model until a preset maximum number of iterations is met, and output a final denoised image with suppressed speckle noise.

[0034] In this embodiment of the present invention, single-view complex image data is first subjected to sub-aperture segmentation to obtain multiple sub-aperture images. A full-aperture image is then obtained through modulo and quantization. The full-aperture image and multiple sub-aperture images are then stacked into a three-dimensional tensor. Based on a preset denoising objective function model, the transformed three-dimensional tensor is then iteratively updated, including global low-rank regularization, non-local denoising regularization, and edge-preserving regularization, to obtain the final denoised image. This method not only effectively suppresses speckle noise but also better preserves image detail features, effectively improving SAR image quality and thus significantly promoting the in-depth application and continued development of SAR technology in various fields.

[0035] Described below Figure 1 How to perform the steps shown.

[0036] First, with respect to step 100 , sub-aperture segmentation processing and modulo quantization processing are performed on the single-view complex image data of the SAR image, respectively, to obtain a sub-aperture image and a full-aperture image of the single-view complex image data in sequence.

[0037] Considering that sub-aperture images obtained by performing sub-aperture decomposition on single-look complex image data of a SAR image can serve as additional information to assist in speckle noise suppression in SAR images, an embodiment of the present invention decomposes and processes the sub-aperture images by the following method: performing Fourier transform on the single-look complex image data along the azimuth direction to obtain a frequency domain signal converted from the spatial domain to the range-Doppler domain; performing energy compensation processing on the spectrum of the frequency domain signal based on the energy distribution in the azimuth direction in the range-Doppler domain so that non-edge regions of the spectrum have uniform energy; equally dividing the compensated spectrum according to a preset number of sub-aperture images to obtain a plurality of sub-aperture bands; wherein adjacent sub-apertures overlap with each other, and the overlapping bandwidth is half the sub-aperture bandwidth; performing sidelobe suppression processing on all the sub-aperture bands according to a preset Hamming window, and performing an inverse Fourier transform on the processed results to quantize and obtain sub-aperture images of the single-look complex image data.

[0038] Specifically, first, the single-view complex image data is Fourier transformed along the azimuth direction to convert it to the range-Doppler domain. In this process, the discrete Fourier transform (DFT) formula is used for transformation:

[0039]

[0040] Among them, f(n) is the original time domain signal, F(u) is the transformed frequency domain signal, N is the number of azimuth points, u is the frequency index, and n is the time domain index, realizing the conversion from the spatial domain to the range-Doppler domain.

[0041] Then, according to the energy distribution in the azimuth direction in the range-Doppler domain, the spectrum is energy compensated so that the energy in the non-edge region of the azimuth spectrum remains basically consistent.

[0042] Specific reference Figure 2 and Figure 3Based on the set number of subaperture images, x, the Doppler spectrum is divided into x subbands, with the overlapping bandwidth of each adjacent subband being 50% of the subaperture bandwidth. This setting allows for increased information acquisition while reducing inter-subaperture correlation. Experimental analysis shows a nonlinear relationship between the number of subapertures, N, and noise reduction performance. As the number of subapertures increases, the relative resolution of the denoised SAR image increases, initially increasing and then decreasing. Multiple comparative experiments were conducted, processing multiple SAR images using different numbers of subapertures. In each experiment, only the number of subapertures was varied, keeping all other parameters constant. The noise reduction effect and image resolution preservation were evaluated using different numbers of subapertures by calculating metrics such as image resolution and equivalent number of views (ENL) of the processed images. Analysis of multiple experimental data sets revealed that a subaperture of 5 effectively suppresses speckle noise while maintaining image resolution, resulting in the best overall performance.

[0043] The remaining frequency bands are then truncated and zeroed using a Hamming window to obtain range-Doppler domain data for different subapertures. This Hamming window truncation effectively suppresses spectral sidelobes and reduces interference between subapertures.

[0044] Finally, the processed range-Doppler domain data is converted back to the spatial domain through inverse Fourier transform, and several sub-aperture images are obtained through image quantization.

[0045] In the embodiment of the present invention, the full-aperture image is obtained by performing modulo and quantization on the single-view complex image data. The specific process of this operation is well known to those skilled in the art and will not be described in detail here.

[0046] With respect to step 102 , a logarithmic transformation is performed on the original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor.

[0047] In the embodiment of the present invention, the original three-dimensional tensor composed of the sub-aperture image and the full-aperture image is first logarithmically transformed. This transformation can convert the speckle noise property of the image from multiplicative noise to additive noise. The transformation formula is:

[0048]

[0049] Among them, A is the original three-dimensional tensor, Y is the tensor after logarithmic transformation, M is the noise-free three-dimensional tensor under ideal conditions, and X is the tensor after logarithmic transformation of M. and N represents noise.

[0050] For step 104, a denoising objective function model including a global low-rank regularization term, a non-local denoising regularization term, and an edge-preserving regularization term is established based on the noisy three-dimensional tensor. The denoising objective function model is iteratively updated until a preset maximum number of iterations is met, and a final denoised image with suppressed speckle noise is output.

[0051] According to the global low-rank characteristics of sub-aperture images and full-aperture images, a denoising objective function model is established, which contains three sub-formulas: global low-rank regularization term, non-local denoising regularization term and edge-preserving regularization term:

[0052]

[0053] Among them, F represents the global low-rank regularization term; NL represents the non-local denoising regularization term; S represents the edge-preserving regularization term; Y is the noisy three-dimensional tensor after logarithmic transformation, is the denoising and dimension reduction tensor, B is the orthogonal basis, and B * is the optimal solution of the denoising objective function model, λ1 and λ2 are the coefficients of the non-local denoising regularization term and the edge preserving regularization term, respectively.

[0054] For this denoising objective function model, the three sub-formulas are not calculated by summing them up. Instead, each sub-formula serves as a constraint condition, jointly controlling and adjusting the optimal solution of the model. In other words, in order to obtain the optimal denoising tensor, it needs to be processed by these three sub-formulas to ensure the best quality of the denoised image.

[0055] In an embodiment of the present invention, the iterative update process includes the following steps: S1, performing a singular value decomposition calculation of a modulo-three expansion on a noisy three-dimensional tensor to obtain a reduced dimensionality tensor and an orthogonal basis to satisfy the global low-rank regularization term constraint after relaxation processing; S2, performing non-local denoising processing on the reduced dimensionality tensor to obtain a denoised reduced dimensionality tensor, and performing dimensionality increase processing on the denoised reduced dimensionality tensor according to the orthogonal basis to obtain a denoised three-dimensional tensor to satisfy the non-local denoising regularization term constraint; S3, performing edge preservation processing on the denoised three-dimensional tensor and the noisy three-dimensional tensor to obtain an updated three-dimensional tensor to satisfy the edge-preserving regularization term constraint; S4, repeating the iterative update of steps S1-S3 with the updated three-dimensional tensor as the noisy three-dimensional tensor, and quantizing the denoised three-dimensional tensor obtained from the last round of iterative updating to obtain the final denoised image.

[0056] Specifically, step S1 is used to perform global low-rank regularization on the tensor. There is strong global spatial similarity between the full-aperture and sub-aperture SAR images. Despite the influence of speckle noise, the three-dimensional tensor they form has a low-rank property overall. This low-rank property means that data in a high-dimensional space can be approximated using a lower-dimensional subspace. In the absence of noise, an ideal SAR image should have a lower rank, but the presence of noise increases the rank.

[0057] From this, the objective function of the global low-rank regularization term can be established as:

[0058]

[0059] In order to optimize the objective function, and considering that the noise level is reduced after iterative regularization, it is relaxed to:

[0060]

[0061] in ×3 It means to perform matrix multiplication on the third dimension of the tensor to achieve dimensionality transformation; I is the unit matrix.

[0062] By performing singular value decomposition on the modulo 3 expansion of Y, that is, (Y) (3) =USV T Let the dimension after dimensionality reduction be dim, then select the number of columns corresponding to dim in V to construct B, that is, B = V(:,1:dim), and further calculation yields The dimension is M×N×dim, which can simplify complex optimization problems into singular value decomposition and simplify calculations.

[0063] It is worth noting that the dimensionality reduction mentioned in the above process is relative to the dimension of the original tensor. For example, at the beginning, 5 sub-aperture images and 1 full-aperture image are combined into an original three-dimensional tensor Y (M×N×6). After the dimensionality reduction transformation, the reduced dimensionality tensor is obtained. and an orthogonal basis B(3×6).

[0064] Furthermore, the role of the non-local denoising regularization term is mainly to remove noise, and the dimensionality reduction tensor obtained after the above processing and the corresponding orthogonal basis are subjected to non-local denoising processing, including the following steps: the dimensionality reduction image corresponding to the dimensionality reduction tensor is divided into blocks to obtain multiple image blocks; the similarity (such as Euclidean distance) between each image block and other image blocks is calculated, and the image blocks are divided into different image groups according to the calculation results, for example, image blocks with higher similarity are divided into a group, thereby obtaining different image block groups; similar image blocks in each non-local group are weighted, and the weighted image block matrix is subjected to nuclear norm minimization calculation to obtain a denoised dimensionality reduction image and its corresponding denoised dimensionality reduction tensor.

[0065] Specifically, the denoising process in this embodiment uses the weighted nuclear norm minimization (WNNM) method. This method achieves denoising by weighting similar image blocks and then performing a nuclear norm minimization operation on the weighted image block matrix. This method is based on the similarity of similar image blocks in low-rank structure and can effectively remove noise.

[0066] Furthermore, the edge-preserving regularization term is implemented after non-local denoising to preserve edge information, prevent over-smoothing, and retain a certain degree of strong scattering points. The specific operation is to first assign different weights based on the neighborhood pixel value distribution of the pixel point in the denoised image to obtain an edge-like image w1(i,j):

[0067]

[0068] Where I0 represents a noisy 3D image, I1 represents a denoised 3D image; (i, j) is the coordinate of the pixel in the image; w0 is a temporary variable before the edge-like image is truncated and normalized; the value of w1 is between 0 and 1, which is equivalent to a weight with the same size as the full image; S represents a window area centered on the pixel I1(i, j). is the average pixel value in area S, h is the attenuation coefficient, and T is the truncation threshold.

[0069] Then the denoised 3D image, the edge-like image and the noisy 3D image are regularized to obtain the updated 3D tensor corresponding to the updated image.

[0070]

[0071] After processing the above three sub-formulas, an updated three-dimensional tensor can be obtained in one round of iteration. The updated three-dimensional tensor output in this round is then used as input to repeat the above process until the preset number of iterations is met. The default setting is three iterations. In the last round of iteration, the denoised dimensionality-reduced tensor obtained by non-local denoising is output as the optimal solution. After dimensionality increase and processing, the final denoised image with suppressed speckle noise is obtained.

[0072] The noise reduction effect of the method proposed in the embodiment of the present invention is as follows: Figure 4 and Figure 5 As shown, Figure 4 This is a SAR image with obvious speckle noise. Figure 5 The SAR image after noise reduction processing by this method is shown in Figure 2. Comparing the two, it can be seen that the clarity and image quality of the image processed by this method are significantly higher than those of the unprocessed image, and the detailed features of the image do not disappear after processing, which shows the effectiveness and practicality of this method.

[0073] Please refer to Figure 6 The embodiment of the present invention provides a SAR image speckle noise suppression device based on subaperture decomposition, the device comprising:

[0074] A processing module 600 is configured to perform sub-aperture segmentation processing and modulus quantization processing on the single-view complex image data of the SAR image, thereby sequentially obtaining a sub-aperture image and a full-aperture image of the single-view complex image data;

[0075] A generating module 602 is configured to perform logarithmic transformation on an original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor;

[0076] An updating module 604 is configured to establish, based on the noisy three-dimensional tensor, a denoising objective function model comprising a global low-rank regularization term, a non-local denoising regularization term, and an edge-preserving regularization term, iteratively update the denoising objective function model until a preset maximum number of iterations is satisfied, and output a final denoised image with suppressed speckle noise.

[0077] In the embodiment of the present invention, when the processing module 600 performs sub-aperture segmentation processing on the single-view complex image data to obtain the sub-aperture image of the single-view complex image data, it is specifically configured to perform the following operations:

[0078] Performing Fourier transform on the single-view complex image data along the azimuth direction to obtain a frequency domain signal converted from the spatial domain to the range-Doppler domain;

[0079] performing energy compensation processing on the spectrum of the frequency domain signal according to the energy distribution in the azimuth direction in the range-Doppler domain, so that non-edge regions of the spectrum have the same energy;

[0080] The compensated spectrum is equally divided according to a preset number of sub-aperture images to obtain a plurality of sub-aperture bands; wherein adjacent sub-apertures overlap with each other, and the overlapping bandwidth is half of the sub-aperture bandwidth;

[0081] Sidelobe suppression processing is performed on all the sub-aperture bands according to a preset Hamming window, and an inverse Fourier transform is performed on the processing results to quantize and obtain the sub-aperture image of the single-view complex image data.

[0082] In the embodiment of the present invention, when the updating module 604 iteratively updates the denoising objective function model until a preset maximum number of iterations is satisfied and outputs a final denoised image with suppressed speckle noise, the updating module 604 is specifically configured to perform the following operations:

[0083] S1. Perform singular value decomposition of the modulo-3 expansion of the noisy three-dimensional tensor to obtain a reduced-dimensional tensor and an orthogonal basis to satisfy the relaxed global low-rank regularization constraint.

[0084] S2. Performing non-local denoising on the reduced-dimensionality tensor to obtain a denoised reduced-dimensionality tensor, and performing dimensionality-increasing processing on the denoised reduced-dimensionality tensor according to the orthogonal basis to obtain a denoised three-dimensional tensor to satisfy the non-local denoising regularization term constraint;

[0085] S3. Performing edge-preserving processing on the denoised three-dimensional tensor and the noisy three-dimensional tensor to obtain an updated three-dimensional tensor to satisfy the edge-preserving regularization term constraint;

[0086] S4. Repeat the iterative update of steps S1 to S3 using the updated three-dimensional tensor as the noisy three-dimensional tensor, and quantize the denoised three-dimensional tensor obtained in the last round of iterative update to obtain the final denoised image.

[0087] In the embodiment of the present invention, the noise reduction objective function model is established by the following formula:

[0088]

[0089] Among them, F represents the global low-rank regularization term; NL represents the non-local denoising regularization term; S represents the edge-preserving regularization term; Y is the noisy three-dimensional tensor after logarithmic transformation, is the denoising and dimension reduction tensor, B is the orthogonal basis, and B * is the optimal solution of the denoising objective function model, λ1 and λ2 are the coefficients of the non-local denoising regularization term and the edge preserving regularization term, respectively.

[0090] In the embodiment of the present invention, when the updating module 604 performs non-local denoising on the reduced dimensionality tensor to obtain the denoised reduced dimensionality tensor, it is specifically configured to perform the following operations:

[0091] Performing block processing on the reduced-dimensionality image corresponding to the reduced-dimensionality tensor to obtain a plurality of image blocks;

[0092] Calculating the similarity between each image block and other image blocks, and grouping the image blocks into different image groups based on the calculation results;

[0093] Weighted processing is performed on similar image blocks in each non-local group, and a nuclear norm minimization calculation is performed on the weighted image block matrix to obtain a denoised and reduced-dimensionality image and its corresponding denoised and reduced-dimensionality tensor.

[0094] In the embodiment of the present invention, when the updating module 604 performs edge-preserving processing on the denoised three-dimensional tensor and the noisy three-dimensional tensor to obtain the updated three-dimensional tensor, it is specifically configured to perform the following operations:

[0095] Obtaining a neighborhood pixel value distribution of a pixel point in the denoised three-dimensional image corresponding to the denoised three-dimensional tensor;

[0096] According to the preset weight value of each neighborhood pixel value, the edge image w1(i,j) is calculated:

[0097]

[0098] Wherein, (i, j) is the coordinate of the pixel point in the image, w0 is the temporary variable before the truncation and normalization processing of the edge-like image; T is the preset truncation threshold;

[0099] An updated three-dimensional tensor corresponding to the updated image is calculated based on the denoised three-dimensional image, the edge-like image and the noisy three-dimensional image corresponding to the noisy three-dimensional tensor.

[0100]

[0101] Where I0 represents a noisy 3D image, and I1 represents a denoised 3D image.

[0102] It should be noted that the SAR image speckle noise suppression device based on subaperture decomposition provided in the above embodiment is merely illustrated by the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the SAR image speckle noise suppression device based on subaperture decomposition provided in the above embodiment and the SAR image speckle noise suppression method based on subaperture decomposition provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0103] The embodiment of the present application also provides a computer device, please refer to Figure 7 The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the SAR image speckle noise suppression method based on subaperture decomposition provided by the above-mentioned method embodiments.

[0104] Embodiments of the present application further provide a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the SAR image speckle noise suppression method based on subaperture decomposition provided in the above-mentioned method embodiments.

[0105] An embodiment of the present application further provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the SAR image speckle noise suppression method based on subaperture decomposition described in any of the above embodiments.

[0106] For the convenience of description, the above systems or devices are described as being divided into various modules or units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0107] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application or certain parts of the embodiments.

[0108] Finally, it should be noted that, in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0109] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for suppressing speckle noise in SAR images based on subaperture decomposition, characterized in that: The method comprises: performing sub-aperture segmentation processing and modulus quantization processing on the single-view complex image data of the SAR image, respectively, to obtain a sub-aperture image and a full-aperture image of the single-view complex image data in sequence; Performing logarithmic transformation on an original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor; A denoising objective function model including a global low-rank regularization term, a non-local denoising regularization term, and an edge-preserving regularization term is established according to the noisy three-dimensional tensor. The denoising objective function model is iteratively updated until a preset maximum number of iterations is met, and a final denoised image with suppressed speckle noise is output.

2. The method according to claim 1, wherein Performing sub-aperture segmentation processing on the single-view complex image data to obtain a sub-aperture image of the single-view complex image data includes: Performing Fourier transform on the single-view complex image data along the azimuth direction to obtain a frequency domain signal converted from the spatial domain to the range-Doppler domain; performing energy compensation processing on the spectrum of the frequency domain signal according to the energy distribution in the azimuth direction in the range-Doppler domain, so that non-edge regions of the spectrum have the same energy; The compensated spectrum is equally divided according to a preset number of sub-aperture images to obtain a plurality of sub-aperture bands; wherein adjacent sub-apertures overlap with each other, and the overlapping bandwidth is half of the sub-aperture bandwidth; Sidelobe suppression processing is performed on all the sub-aperture bands according to a preset Hamming window, and an inverse Fourier transform is performed on the processing results to quantize and obtain the sub-aperture image of the single-view complex image data.

3. The method according to claim 1, wherein The iterative updating of the noise reduction objective function model until a preset maximum number of iterations is satisfied, and outputting a final noise reduction image with suppressed speckle noise, comprises: S1. Perform singular value decomposition of the modulo-3 expansion of the noisy three-dimensional tensor to obtain a reduced-dimensional tensor and an orthogonal basis to satisfy the relaxed global low-rank regularization constraint. S2. Performing non-local denoising on the reduced-dimensionality tensor to obtain a denoised reduced-dimensionality tensor, and performing dimensionality-increasing processing on the denoised reduced-dimensionality tensor according to the orthogonal basis to obtain a denoised three-dimensional tensor to satisfy the non-local denoising regularization term constraint; S3. Performing edge-preserving processing on the denoised three-dimensional tensor and the noisy three-dimensional tensor to obtain an updated three-dimensional tensor to satisfy the edge-preserving regularization term constraint; S4. Repeat the iterative update of steps S1 to S3 using the updated three-dimensional tensor as the noisy three-dimensional tensor, and quantize the denoised three-dimensional tensor obtained in the last round of iterative update to obtain the final denoised image.

4. The method according to claim 3, wherein The noise reduction objective function model is established by the following formula: Among them, F represents the global low-rank regularization term; NL represents the non-local denoising regularization term; S represents the edge-preserving regularization term; Y is the noisy three-dimensional tensor after logarithmic transformation, is the denoising and dimension reduction tensor, B is the orthogonal basis, and B * is the optimal solution of the denoising objective function model, λ1 and λ2 are the coefficients of the non-local denoising regularization term and the edge preserving regularization term, respectively.

5. The method according to claim 3, wherein The performing non-local denoising on the reduced-dimensionality tensor to obtain a denoised reduced-dimensionality tensor includes: Performing block processing on the reduced-dimensionality image corresponding to the reduced-dimensionality tensor to obtain a plurality of image blocks; Calculating the similarity between each image block and other image blocks, and grouping the image blocks into different image groups based on the calculation results; Weighted processing is performed on similar image blocks in each non-local group, and a nuclear norm minimization calculation is performed on the weighted image block matrix to obtain a denoised and reduced-dimensionality image and its corresponding denoised and reduced-dimensionality tensor.

6. The method according to claim 3, wherein The performing edge-preserving processing on the denoised three-dimensional tensor and the noisy three-dimensional tensor to obtain an updated three-dimensional tensor includes: Obtaining a neighborhood pixel value distribution of a pixel point in the denoised three-dimensional image corresponding to the denoised three-dimensional tensor; According to the preset weight value of each neighborhood pixel value, the edge image w1(i,j) is calculated: Wherein, (i, j) is the coordinate of the pixel point in the image, w0 is the temporary variable before the truncation and normalization processing of the edge-like image; T is the preset truncation threshold; An updated three-dimensional tensor corresponding to the updated image is calculated based on the denoised three-dimensional image, the edge-like image and the noisy three-dimensional image corresponding to the noisy three-dimensional tensor. Where I0 represents a noisy 3D image, and I1 represents a denoised 3D image.

7. A SAR image speckle noise suppression device based on subaperture decomposition, characterized in that: The device comprises: a processing module, configured to perform sub-aperture segmentation processing and modulus quantization processing on the single-view complex image data of the SAR image, and sequentially obtain a sub-aperture image and a full-aperture image of the single-view complex image data; a generating module, performing logarithmic transformation on an original three-dimensional tensor composed of the sub-aperture image and the full-aperture image to generate a noisy three-dimensional tensor; An updating module is configured to establish, based on the noisy three-dimensional tensor, a denoising objective function model including a global low-rank regularization term, a nonlocal denoising regularization term, and an edge-preserving regularization term, and iteratively update the denoising objective function model until a preset maximum number of iterations is met, thereby outputting a final denoised image with suppressed speckle noise.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of any one of the methods described in claims 1-6.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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