Side scan sonar image denoising method and system based on empirical mode decomposition and visual shrinkage threshold

Abstract

The present application relates to the technical field of image noise reduction processing, in particular to a side scan image noise reduction method and system based on empirical mode decomposition and visual shrinkage threshold, first, obtaining a side scan sonar image containing noise; secondly, performing FastBEMD adaptive decomposition on the side scan sonar image containing noise to obtain BIMF1 layer, intermediate layer and residual layer; then, based on the VisuShrink criterion, performing noise component filtering on the BIMF1 layer to obtain a noise-reduced BIMF1 layer; finally, superimposing and reconstructing the noise-reduced BIMF1 layer, the intermediate layer and the residual layer to obtain a noise-reduced side scan sonar image. The present application performs visual shrinkage threshold filtering on the BIMF1 layer, which can not only eliminate image noise but also maintain the edge details of the image target, avoiding the problem of side scan sonar image target edge blunting.

Description

Technical Field

[0001] This invention relates to the field of image denoising technology, and in particular to a side-scan image denoising method and system based on empirical mode decomposition and visual shrinkage threshold. Background Technology

[0002] During underwater detection and imaging, side-scan sonar images are susceptible to factors such as water scattering, seabed reverberation, equipment circuit interference, and ocean current disturbances, resulting in a large amount of random noise. Various types of noise not only mask the echo signals of weak underwater targets and blur the edge contours of targets such as shipwrecks, pipelines, and reefs, but also disrupt the image grayscale distribution, confuse different seabed characteristics, and severely degrade the quality of sonar images. Furthermore, noise significantly interferes with subsequent image segmentation, feature extraction, and target recognition, easily leading to missed detections and misjudgments, reducing the accuracy of underwater survey data. Therefore, noise reduction processing of side-scan sonar images is essential. Effective noise reduction can filter out irrelevant interference information, restore the true seabed topography and target morphology, and clearly highlight the characteristics of underwater targets. It also provides high-quality image data for practical operations such as seabed topographic mapping, marine geological surveys, and underwater engineering exploration, and improves the accuracy of subsequent intelligent interpretation algorithms. It is an indispensable preliminary step in side-scan sonar underwater acoustic image processing.

[0003] The existing BEMD denoising method traverses all pixels of the image to retrieve all extreme points, obtains each IBMF layer, and directly removes the BIMF1 layer, which leads to the problem of blunting the target edges in the side-scan sonar image. Summary of the Invention

[0004] The purpose of this invention is to provide a side-scan image denoising method and system based on empirical mode decomposition and visual shrinkage threshold, so as to achieve visual shrinkage threshold filtering of BIMF1 layer.

[0005] To achieve the above objectives, this invention provides a side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold, the method comprising:

[0006] Acquire noisy side-scan sonar images;

[0007] The noisy side-scan sonar image is subjected to FastBEMD adaptive decomposition to obtain two-dimensional intrinsic mode components; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer;

[0008] Based on the VisuShrink criterion, noise components are filtered out from the BIMF1 layer to obtain the noise-reduced BIMF1 layer.

[0009] The denoised BIMF1 layer, the intermediate layer, and the residual layer are superimposed and reconstructed to obtain the denoised side-scan sonar image.

[0010] Optionally, the step of performing FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components specifically includes:

[0011] The upper and lower envelopes of the noisy side-scan sonar image are calculated using the sequential statistical filtering method.

[0012] The upper envelope surface and the lower envelope surface are obtained based on the upper envelope and the lower envelope, respectively;

[0013] Based on the upper envelope surface and the lower envelope surface, two-dimensional intrinsic mode components are obtained through iterative sieving;

[0014] Determine whether the decomposition termination condition has been met; if the decomposition termination condition has been met, output the two-dimensional intrinsic mode components; if the decomposition termination condition has not been met, return to the step of using the sequential statistical filtering method to calculate the upper and lower envelopes of the local area of ​​the noisy side-scan sonar image respectively.

[0015] Optionally, the step of filtering out noise components from the BIMF1 layer based on the VisuShrink criterion to obtain a denoised BIMF1 layer specifically includes:

[0016] Based on the VisuShrink criterion, a global adaptive soft threshold calculation is performed on the BIMF1 layer;

[0017] The wavelet coefficient matrix is ​​filtered for noise components based on the global adaptive soft threshold to obtain the denoised BIMF1 layer.

[0018] Optionally, the calculation of a globally adaptive soft threshold for the BIMF1 layer based on the VisuShrink criterion specifically includes:

[0019] Wavelet decomposition is performed on the BIMF1 layer to obtain the wavelet coefficient matrix;

[0020] Using the wavelet coefficient matrix, the noise standard deviation is robustly estimated based on the median method;

[0021] The global adaptive soft threshold is calculated based on the noise standard deviation according to the VisuShrink criterion.

[0022] Optionally, the step of filtering out noise components from the wavelet coefficient matrix based on the global adaptive soft threshold to obtain the denoised BIMF1 layer specifically includes:

[0023] The noise components in the wavelet coefficient matrix are filtered out by the global adaptive soft thresholding to obtain a noise-free coefficient matrix.

[0024] The noise-free coefficient matrix is ​​reconstructed by wavelet inverse transformation to obtain the denoised BIMF1 layer.

[0025] Optionally, determining whether the decomposition termination condition has been met specifically includes:

[0026] Determine if the total residual value is less than a set threshold; if it is less than the set threshold, the decomposition termination condition has been met; if it is greater than or equal to the set threshold, determine if the number of decomposition layers has reached a preset number; if it has reached the preset number, the decomposition termination condition has been met; if it has not reached the preset number, the decomposition termination condition has not been met.

[0027] Optionally, determining whether the decomposition termination condition has been met specifically includes:

[0028] Determine if the number of decomposition layers has reached the preset number of layers; if it has, it means that the decomposition termination condition has been met; if it has not reached the preset number of layers, determine if the total residual value is less than the set threshold; if it is less than the set threshold, it means that the decomposition termination condition has been met; if it is greater than or equal to the set threshold, it means that the decomposition termination condition has not been met.

[0029] The present invention also provides a side-scan image denoising system based on empirical mode decomposition and visual shrinkage threshold, the system comprising:

[0030] The acquisition module is used to acquire noisy side-scan sonar images;

[0031] An adaptive decomposition module is used to perform FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer;

[0032] The noise component filtering module is used to filter out noise components in the BIMF1 layer based on the VisuShrink criterion to obtain the noise-reduced BIMF1 layer.

[0033] The overlay and reconstruction module is used to overlay and reconstruct the denoised BIMF1 layer, the intermediate layer and the residual layer to obtain the denoised side-scan sonar image.

[0034] The present invention also provides a computer-storable medium storing a computer program, which, when executed by a processor, implements the steps of the side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold as described above.

[0035] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold.

[0036] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0037] This invention provides a side-scan image denoising method and system based on empirical mode decomposition and visual shrinkage threshold. First, a noisy side-scan sonar image is acquired. Second, the noisy side-scan sonar image is subjected to FastBEMD adaptive decomposition to obtain a BIMF1 layer, an intermediate layer, and a residual layer. Then, based on the VisuShrink criterion, noise components are filtered out from the BIMF1 layer to obtain a denoised BIMF1 layer. Finally, the denoised BIMF1 layer, intermediate layer, and residual layer are superimposed and reconstructed to obtain the denoised side-scan sonar image. This invention applies a visual shrinkage threshold filter to the BIMF1 layer, which can eliminate image noise while preserving the edge details of the image target, avoiding the problem of target edge blunting in side-scan sonar images. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of the side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to an embodiment of the present invention;

[0040] Figure 2 The target side scan image includes noise in an embodiment of the present invention;

[0041] Figure 3 This refers to the BIMF1 layer image corresponding to the target side scan image in this embodiment of the invention;

[0042] Figure 4 This is the denoised image corresponding to the target side-scan image in this embodiment of the invention;

[0043] Figure 5 This is a side-scan image of anchor marks containing noise, as described in an embodiment of the present invention.

[0044] Figure 6 This is the BIMF1 layer image corresponding to the anchor mark side scan image in an embodiment of the present invention;

[0045] Figure 7 This is the denoised image corresponding to the anchor mark side scan image in an embodiment of the present invention;

[0046] Figure 8 This is a side-scan image of sand wave terrain containing noise, as described in an embodiment of the present invention.

[0047] Figure 9 This is the BIMF1 layer image corresponding to the sand wave topography side scan image in an embodiment of the present invention;

[0048] Figure 10 This is the denoised image corresponding to the side-scan image of the sand wave terrain in an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The purpose of this invention is to provide a side-scan image denoising method and system based on empirical mode decomposition and visual shrinkage threshold, so as to achieve visual shrinkage threshold filtering of BIMF1 layer.

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] In the following scheme, FastBEMD represents fast two-dimensional empirical modality decomposition, and VisuShrink represents fast visual shrinkage. For ease of writing, the following text will be written in English.

[0053] like Figure 1 As shown, this invention discloses a side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold, the method comprising:

[0054] Step S1: Acquire noisy side-scan sonar images.

[0055] Step S2: Perform FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer.

[0056] Step S3: Based on the VisuShrink criterion, noise components are filtered out from the BIMF1 layer to obtain the denoised BIMF1 layer.

[0057] Step S4: Overlay and reconstruct the denoised BIMF1 layer, intermediate layer and residual layer to obtain the denoised side-scan sonar image.

[0058] The following is a detailed discussion of each step:

[0059] Step S2: Perform FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components, specifically including:

[0060] Step S21: Using the sequential statistical filtering method, calculate the upper and lower envelopes of the local area of ​​the noisy side-scan sonar image respectively;

[0061] Step S22: Calculate the upper envelope surface and the lower envelope surface based on the upper envelope and the lower envelope respectively.

[0062] Step S23: Based on the upper and lower envelope surfaces, obtain the two-dimensional intrinsic mode components through iterative sieving; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer; the BIMF1 layer is the first two-dimensional intrinsic mode component layer, and this layer has the highest frequency, so this layer can also be called the high-frequency BIMF1 layer; the remaining two-dimensional intrinsic mode component layers are collectively called intermediate layers, and the frequency of the intermediate layers decreases layer by layer, so they can also be called mid-low frequency BIMF layers.

[0063] Step S24: Determine whether the decomposition termination condition has been met; if the decomposition termination condition has been met, output the two-dimensional intrinsic mode components; if the decomposition termination condition has not been met, return to step S21.

[0064] Determining whether the decomposition termination condition has been met includes:

[0065] Determine if the total residual value is less than a set threshold; if it is less than the set threshold, the decomposition termination condition has been met; if it is greater than or equal to the set threshold, determine if the number of decomposition layers has reached a preset number; if it has reached the preset number, the decomposition termination condition has been met; if it has not reached the preset number, the decomposition termination condition has not been met.

[0066] Alternatively, determine whether the number of decomposition layers has reached the preset number of layers; if the preset number of layers has been reached, it means that the decomposition termination condition has been met; if the preset number of layers has not been reached, determine whether the total residual value is less than the set threshold; if it is less than the set threshold, it means that the decomposition termination condition has been met; if it is greater than or equal to the set threshold, it means that the decomposition termination condition has not been met.

[0067] Step S3: Based on the VisuShrink criterion, noise components are filtered out from the BIMF1 layer to obtain the denoised BIMF1 layer, specifically including:

[0068] Step S31: Based on the VisuShrink criterion, perform global adaptive soft threshold calculation on the BIMF1 layer, specifically including:

[0069] Step S311: Perform wavelet decomposition on the BIMF1 layer to obtain the wavelet coefficient matrix C.

[0070] Step S312: Using the wavelet coefficient matrix C, robustly estimate the noise standard deviation σ based on the median method. The specific formula is as follows:

[0071] ;

[0072] Where C is the wavelet coefficient matrix, σ is the noise standard deviation, and median() is the median function.

[0073] Step S313: Calculate the global adaptive soft threshold thr based on the noise standard deviation according to the VisuShrink criterion. The specific formula is as follows:

[0074] ;

[0075] Where N is the total number of wavelet coefficients. σ is the global adaptive soft threshold, and σ is the noise standard deviation.

[0076] Step S32: Noise components are filtered out from the wavelet coefficient matrix based on a globally adaptive soft threshold to obtain the denoised BIMF1 layer, specifically including:

[0077] Step S321: Noise components in the wavelet coefficient matrix are filtered out using a global adaptive soft threshold to obtain a noise-free coefficient matrix. The specific formula is as follows:

[0078] ;

[0079] in, C is the noise-free coefficient matrix, and C is the wavelet coefficient matrix. It is a globally adaptive soft threshold.

[0080] Step S322: For the noise-free coefficient matrix Wavelet inverse reconstruction is performed to obtain the denoised BIMF1 layer.

[0081] Step S4: Overlay and reconstruct the denoised BIMF1 layer, intermediate layer, and residual layer to obtain the denoised side-scan sonar image. The specific formula is as follows:

[0082] ;

[0083] in, This is a side-scan sonar image after noise reduction. This is the BIMF1 layer after noise reduction. This is the i-th two-dimensional intrinsic mode component layer. The m-1 two-dimensional intrinsic mode component layers are called intermediate layers. It is a residual layer.

[0084] This invention calculates the peak signal-to-noise ratio (PNSR) of the denoised side-scan sonar image to evaluate the image denoising effect. The specific formula is as follows:

[0085] ;

[0086] ;

[0087] in, Peak signal-to-noise ratio, Let be the mean square error of the denoised side-scan sonar image, and M and N be the length and width of the denoised side-scan sonar image, respectively. Let be the pixel value in the i-th row and j-th column of the noisy side-scan sonar image. is the pixel value of the i-th row and j-th column of the side-scan sonar image after noise reduction.

[0088] This invention can clearly evaluate the image denoising effect using the signal-to-noise ratio. Other methods can also be used to evaluate the image denoising effect, but specific examples are not given here.

[0089] The method disclosed in this invention uses a fast two-dimensional empirical mode decomposition method for adaptive decomposition. Based on the set region size, it can quickly lock local extreme points, which greatly improves the computational efficiency. In order to avoid the problem of blunting the target edge of the side-scan sonar image due to the indiscriminate removal of the high-frequency BIMF1 layer in the traditional method, this invention performs visual shrinkage threshold filtering on the BIMF1 layer, which can eliminate image noise while maintaining the edge details of the image target.

[0090] The present invention also provides a side-scan image denoising system based on empirical mode decomposition and visual shrinkage threshold, the system comprising:

[0091] The acquisition module is used to acquire noisy side-scan sonar images.

[0092] An adaptive decomposition module is used to perform FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer.

[0093] The noise component filtering module is used to filter out noise components from the BIMF1 layer based on the VisuShrink criterion to obtain a noise-reduced BIMF1 layer.

[0094] The overlay and reconstruction module is used to overlay and reconstruct the denoised BIMF1 layer, the intermediate layer and the residual layer to obtain the denoised side-scan sonar image.

[0095] The parts that are the same as those in the method will not be discussed again; please refer to the method for details.

[0096] The present invention also provides a computer-storable medium storing a computer program, which, when executed by a processor, implements the steps of the above-described side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold.

[0097] Embodiments of the present invention may be provided as methods, systems, or computer program products. The invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the invention may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold.

[0102] Specific examples:

[0103] Example 1: Side scan image of a target containing noise

[0104] Acquiring noisy target side scan images, such as Figure 2 As shown; FastBEMD adaptive decomposition is performed on the noisy target side scan image to obtain two-dimensional intrinsic mode components, including the BIMF1 layer (e.g. Figure 3 The BIMF1 layer consists of an intermediate layer and a residual layer. Wavelet decomposition is performed on the BIMF1 layer to obtain the wavelet coefficient matrix C. The noise scale σ = 0.10 is robustly estimated based on the median method. The global adaptive soft threshold thr = 0.52 is calculated according to the VisuShrink criterion. Noise components in the wavelet coefficient matrix are filtered out using the global adaptive soft threshold. Then, wavelet inverse reconstruction is used to obtain the denoised BIMF1 layer. denoised Layer, after noise reduction BIMF1 denoised The first layer, the intermediate layer, and the residual layer R are stacked and reconstructed to obtain the denoised side-scan sonar image, as shown below. Figure 4 As shown.

[0105] Calculations show that the PSNR before noise reduction was 20.01 dB, and the PSNR after noise reduction was 25.48 dB. This demonstrates that the noise reduction of the target side-scan image improved by 27.34% compared to before noise reduction.

[0106] Example 2: Side scan image of anchor marks containing noise

[0107] Acquire noisy anchor mark side scan images, such as Figure 5 As shown, FastBEMD adaptive decomposition is performed on the noisy anchor mark side scan image to obtain two-dimensional intrinsic mode components, including the BIMF1 layer (such as...). Figure 6 The BIMF1 layer consists of an intermediate layer and a residual layer. Wavelet decomposition is performed on the BIMF1 layer to obtain the wavelet coefficient matrix C. The noise scale σ = 0.11 is robustly estimated based on the median method. The global adaptive soft threshold thr = 0.56 is calculated according to the VisuShrink criterion. Noise components in the wavelet coefficient matrix are filtered out using the global adaptive soft threshold, and then wavelet inverse reconstruction is used to obtain the denoised BIMF1. denoised Layer, after noise reduction BIMF1 denoised The first layer, the intermediate layer, and the residual layer R are stacked and reconstructed to obtain the denoised side-scan sonar image, as shown below. Figure 7 As shown.

[0108] Calculations showed that the PSNR before denoising was 20.02 dB, and the PSNR after denoising was 23.29 dB. This demonstrates that the denoising of the anchor mark side scan image improved by 16.33% compared to before denoising.

[0109] Example 3: Side scan image of sand wave terrain containing noise

[0110] Side scan images of noisy sand wave terrain, such as Figure 8 As shown, FastBEMD adaptive decomposition is performed on the sand wave topographic side scan image to obtain two-dimensional intrinsic mode components, including the BIMF1 layer (such as...). Figure 9 The BIMF1 layer consists of an intermediate layer and a residual layer. Wavelet decomposition is performed on the BIMF1 layer to obtain the wavelet coefficient matrix C. The noise scale σ = 0.10 is robustly estimated based on the median method. The global adaptive soft threshold thr = 0.55 is calculated according to the VisuShrink criterion. Noise components in the wavelet coefficient matrix are filtered out using the global adaptive soft threshold. Then, wavelet inverse reconstruction is used to obtain the denoised BIMF1 layer. denoised Layer, after noise reduction BIMF1 denoised The first layer, the intermediate layer, and the residual layer R are stacked and reconstructed to obtain the denoised side-scan sonar image, as shown below. Figure 10 As shown.

[0111] Calculations showed that the PSNR before denoising was 20.02 dB, and the PSNR after denoising was 23.34 dB. This demonstrates that the denoising of the sand dune topographic side-scan image improved by 16.58% compared to before denoising.

[0112] The proposed Fast 2D Empirical Mode Decomposition-Fast Visual Shrink (FastBEMD-VisuShrink) wavelet soft threshold side-scan sonar image denoising algorithm is more efficient and robust than traditional BEMD decomposition.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0114] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold, characterized in that, The method includes: Acquire noisy side-scan sonar images; The noisy side-scan sonar image is subjected to FastBEMD adaptive decomposition to obtain two-dimensional intrinsic mode components; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer; Based on the VisuShrink criterion, noise components are filtered out from the BIMF1 layer to obtain the noise-reduced BIMF1 layer. The denoised BIMF1 layer, the intermediate layer, and the residual layer are superimposed and reconstructed to obtain the denoised side-scan sonar image.

2. The side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to claim 1, characterized in that, The step of performing FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components specifically includes: The upper and lower envelopes of the noisy side-scan sonar image are calculated using the sequential statistical filtering method. The upper envelope surface and the lower envelope surface are obtained based on the upper envelope and the lower envelope, respectively; Based on the upper envelope surface and the lower envelope surface, two-dimensional intrinsic mode components are obtained through iterative sieving; Determine whether the decomposition termination condition has been met; if the decomposition termination condition has been met, output the two-dimensional intrinsic mode components; if the decomposition termination condition has not been met, return to the step of using the sequential statistical filtering method to calculate the upper and lower envelopes of the local area of ​​the noisy side-scan sonar image respectively.

3. The side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to claim 1, characterized in that, The step of filtering out noise components from the BIMF1 layer based on the VisuShrink criterion to obtain a denoised BIMF1 layer specifically includes: Based on the VisuShrink criterion, a global adaptive soft threshold calculation is performed on the BIMF1 layer; The noise components of the wavelet coefficient matrix are filtered out based on the global adaptive soft threshold to obtain the denoised BIMF1 layer.

4. The side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to claim 3, characterized in that, The global adaptive soft threshold calculation for the BIMF1 layer based on the VisuShrink criterion specifically includes: Wavelet decomposition is performed on the BIMF1 layer to obtain the wavelet coefficient matrix; Using the wavelet coefficient matrix, the noise standard deviation is robustly estimated based on the median method; The global adaptive soft threshold is calculated based on the noise standard deviation according to the VisuShrink criterion.

5. The side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to claim 3, characterized in that, The step of filtering out noise components from the wavelet coefficient matrix based on the global adaptive soft threshold to obtain the denoised BIMF1 layer specifically includes: The noise components in the wavelet coefficient matrix are filtered out by the global adaptive soft thresholding to obtain a noise-free coefficient matrix. The noise-free coefficient matrix is ​​reconstructed by wavelet inverse transformation to obtain the denoised BIMF1 layer.

6. The side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to claim 2, characterized in that, The determination of whether the decomposition termination condition has been met specifically includes: Determine if the total residual value is less than a set threshold; if it is less than the set threshold, the decomposition termination condition has been met; if it is greater than or equal to the set threshold, determine if the number of decomposition layers has reached a preset number; if it has reached the preset number, the decomposition termination condition has been met; if it has not reached the preset number, the decomposition termination condition has not been met.

7. The side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold according to claim 2, characterized in that, The determination of whether the decomposition termination condition has been met specifically includes: determining whether the number of decomposition layers has reached a preset number of layers; if the preset number of layers has been reached, it means that the decomposition termination condition has been met; if the preset number of layers has not been reached, determining whether the total value of the residual layers is less than a set threshold; if it is less than the set threshold, it means that the decomposition termination condition has been met; if it is greater than or equal to the set threshold, it means that the decomposition termination condition has not been met.

8. A side-scan image denoising system based on empirical mode decomposition and visual shrinkage threshold, characterized in that, The system includes: The acquisition module is used to acquire noisy side-scan sonar images; An adaptive decomposition module is used to perform FastBEMD adaptive decomposition on the noisy side-scan sonar image to obtain two-dimensional intrinsic mode components; the two-dimensional intrinsic mode components include: BIMF1 layer, intermediate layer and residual layer; The noise component filtering module is used to filter out noise components in the BIMF1 layer based on the VisuShrink criterion to obtain the noise-reduced BIMF1 layer. The overlay and reconstruction module is used to overlay and reconstruct the denoised BIMF1 layer, the intermediate layer and the residual layer to obtain the denoised side-scan sonar image.

9. A computer-storable medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the steps of the side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold as described in any one of claims 1-7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the side-scan image denoising method based on empirical mode decomposition and visual shrinkage threshold as described in any one of claims 1-7.

Images