Speckle denoising method, system and device based on iterative wavelet transform and medium

Abstract

The invention belongs to the technical field of image processing, and relates to a speckle denoising method, system and device based on iterative wavelet transform and a medium, and the method comprises the steps: carrying out the wavelet decomposition and reconstruction of an input speckle image, and obtaining a background estimation image; comparing the background estimation image with a deviation threshold value, and carrying out truncation processing on pixels exceeding the threshold value to generate a residual image; after updating iteration, taking a final residual image as background estimation; subtracting the background estimation from the original speckle image to obtain a de-noised speckle image; and carrying out deconvolution operation to obtain a denoised restored image. According to the method, the advantages of wavelet transform are combined, the noise in a complex speckle scene is effectively suppressed, the speckle image is preprocessed, and the multi-scale characteristic of wavelet transform is utilized, so that the noise can be finely removed on different scales, and meanwhile, key details of a target image are reserved.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing and relates to a speckle denoising method, system, equipment and medium based on iterative wavelet transform. Background Art

[0002] In optoelectronic imaging, the inherent physical properties of the imaging system, such as the sensor's dark current effect, optical aberrations, spherical aberration, and other distortion factors, can all cause random noise. This noise severely impacts the quality and contrast of speckle-reconstructed images, significantly reducing the image's usability. Therefore, speckle noise removal is crucial for both research and practical applications in speckle reconstruction.

[0003] Among spatial domain methods, total variation (TV)-based methods are particularly well-suited, as they can smooth internal image structures while preserving edge information. The first TV-based speckle noise removal model achieves denoising using a constrained optimization method involving two Lagrange multipliers.

[0004] In the transform domain, sine and cosine average denoising is the most commonly used speckle denoising method due to its simple process. Based on sine and cosine filtering, many scholars have made improvements to reduce the number of cycles of traditional sine and cosine filtering and reduce the loss of phase image grayscale information.

[0005] Gaussian filtering is also an important filtering method. It smoothes the image based on the characteristics of the Gaussian function and can effectively remove noise. Its principle is to recalculate the value of each pixel in the image based on the weighted average of its neighboring pixels, where the weight is determined by the Gaussian function.

[0006] Wavelet transform is another important speckle denoising method. It decomposes the image into different feature maps through multi-scale decomposition and uses coefficient shrinkage techniques to achieve good denoising effects. In recent years, wavelet transform-based methods have continued to develop. Among them, the improved total variation denoising method based on the shearlet transform includes a denoising regularization term, a weighted edge-preserving regularization term, and a weighted fidelity term. It achieves noise suppression and edge protection by adjusting the weights. The speckle denoising method based on the stationary wavelet transform first performs sine and cosine decomposition and stationary wavelet decomposition on the original image, then processes the high-frequency coefficients according to the threshold rule, and finally performs an inverse tangent operation to obtain the denoised image.

[0007] While diffusion-based methods in the spatial domain achieve good results, they can overfit in the presence of high speckle noise, leading to loss of detail. TV-based methods, which fail to reflect sufficient signal and noise information, are prone to a "staircase" effect. Furthermore, the denoising effect varies significantly between different image regions, affecting the overall denoising effect.

[0008] Although sine and cosine averaging denoising is the most commonly used speckle denoising method due to its simplicity, it suffers from the problem of slower denoising speed as the number of iterations increases. Based on sine and cosine filtering, many researchers have improved upon it, reducing the number of iterations and minimizing the loss of grayscale information in the phase image. However, these algorithms rely on spatial or transform domain denoising and cannot balance phase jumps and detail preservation.

[0009] Gaussian filtering, a commonly used smoothing technique, is also used in speckle noise suppression. However, the speckle restoration results after Gaussian filtering often retain streak artifacts, which not only limits its applicability in certain fields but also indicates that this method may not be effective for certain types of noise.

[0010] Furthermore, wavelet transforms in transform domains fail to fully consider the spatial redundancy of the image, which can easily lead to artifacts and scratches in the image texture, seriously affecting the denoising effect. While a larger number of stationary wavelet decomposition layers improves the smoothing effect in speckle denoising methods based on stationary wavelet transforms, some edge details are lost.

[0011] In summary, the random noise caused by the inherent physical characteristics of the imaging system and environmental factors interferes with the speckle pattern, resulting in poor quality of speckle reconstruction results. Random noise not only introduces a large amount of noise components into the speckle pattern, reducing the stability of the reconstruction results, but also easily produces artifacts and noise amplification, making it more difficult to recover image details. In addition, in high-noise environments, the above problems are particularly prominent, making it difficult to generate high-fidelity speckle reconstruction images, which seriously limits the application of speckle imaging technology in scientific research, biological microscopy, astronomical observation, satellite remote sensing and other fields. Although current speckle denoising technology has shown a booming development trend in multiple dimensions and multiple paths and has achieved certain results, there is still room for optimization and improvement in terms of image quality improvement. Therefore, the development of a new speckle denoising method that can effectively suppress noise while retaining image details and edge information has important theoretical significance and practical application value. Summary of the Invention

[0012] To address the challenges of existing technologies, the present invention proposes a speckle denoising method, system, device, and medium based on iterative wavelet transform. This method leverages the advantages of wavelet transform to effectively suppress noise in complex speckle scenes. By preprocessing the speckle image and leveraging the multi-scale properties of the wavelet transform, this method can precisely remove noise at different scales while preserving key details of the target image. This method not only possesses powerful noise suppression capabilities but also exhibits high algorithmic robustness, making it widely applicable to various fields of speckle image restoration, such as biological microscopy, astronomical imaging, computational photography, and remote sensing image processing.

[0013] The present invention is achieved through the following technical solutions:

[0014] A speckle denoising method based on iterative wavelet transform, comprising:

[0015] Get the original speckle image I 0 , initialize the dynamic deviation threshold T, as the input speckle image I k The average pixel value of

[0016] The input speckle image I k Perform multi-scale wavelet decomposition, retain the lowest frequency coefficients, and set the high frequency coefficients to 0;

[0017] Perform inverse wavelet transform on the lowest frequency coefficient and reconstruct the background estimation image

[0018] The background estimation image Compare with the deviation threshold T, truncate the pixels exceeding the threshold, and generate the residual image I k ;

[0019] The residual image I k As the input of the next round, return to update the dynamic deviation threshold T until the preset number of iterations is reached, and use the final residual image as the background estimation

[0020] Through the original speckle image I 0 Background subtraction estimation Obtain denoised speckle image Denoised speckle image Perform deconvolution operation to obtain the denoised restored image.

[0021] Preferably, the original speckle image contains random noise caused by imaging system noise and environmental interference.

[0022] Preferably, the input speckle image I k Perform multi-scale wavelet decomposition, retain the lowest frequency coefficients, and set the high frequency coefficients to 0, specifically:

[0023] The input speckle image I is filtered using a two-dimensional Daubechies-6 wavelet filter. k Perform n-layer decomposition to obtain the high-frequency coefficients of each layer {cD1,cD2,...,cD n} and the lowest frequency coefficient cA n , only retain the lowest frequency coefficient cA n , and all other high-frequency coefficients are set to zero.

[0024] Preferably, the lowest frequency coefficient is subjected to inverse wavelet transform to reconstruct the background estimation image The expression is:

[0025]

[0026] In the formula, IDWT represents the inverse discrete wavelet transform, k represents the current iteration number, is the lowest frequency coefficient cA retained in the k-th iteration n .

[0027] Preferably, the background estimated image is compared with the dynamic deviation threshold T, and the pixels exceeding the deviation threshold are truncated. The expression is:

[0028]

[0029] The lowest frequency coefficient cA n is compared with the deviation threshold T. The pixel values exceeding the deviation threshold T are set to the threshold T, and the pixel values not exceeding the deviation threshold T remain unchanged to obtain the final speckle image I k .

[0030] Preferably, the residual image I k is used as the input for the next round, and the dynamic deviation threshold T is updated until the preset iteration number is reached. Specifically:

[0031] Set the maximum iteration number iterations, k represents the current iteration number,

[0032] If k < iterations, the residual image I k is used as the input for the next round, and the deviation threshold T is updated;

[0033] If k = iterations, background estimation is performed The expression is:

[0034]

[0035] The denoised speckle image is obtained by subtracting the background estimation from the original speckle image I 0 The expression is:

[0036]

[0037] Preferably, the denoised speckle image is deconvolved to obtain the denoised restored image.

[0038]

[0039] Where deconv(·) represents the deconvolution operation, and PSF represents the point spread function of the system.

[0040] A speckle denoising system based on iterative wavelet transform, comprising:

[0041] Image acquisition module, used to obtain the original speckle image I 0 ;

[0042] Dynamic threshold processing module, used to initialize the dynamic deviation threshold T, as the input speckle image I k The average pixel value of the background estimation image Compare with the deviation threshold T, truncate the pixels exceeding the threshold, and generate the residual image I k ;

[0043] The wavelet processing module is used to transform the input speckle image I k Perform multi-scale wavelet decomposition, retain the lowest frequency coefficients, and set the high frequency coefficients to 0;

[0044] The reconstruction module is used to perform inverse wavelet transform on the lowest frequency coefficients to reconstruct the background estimation image

[0045] Iterative optimization module is used to transform the residual image I k As the input of the next round, return to update the dynamic deviation threshold T until the preset number of iterations is reached, and use the final residual image as the background estimation

[0046] Image restoration module, used to restore the original speckle image I 0 Background subtraction estimation Obtain denoised speckle image Denoised speckle image Perform deconvolution operation to obtain the target restored image.

[0047] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the speckle denoising method based on iterative wavelet transform are implemented.

[0048] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the speckle denoising method based on iterative wavelet transform.

[0049] Compared with the prior art, the present invention has the following beneficial technical effects:

[0050] This paper proposes a speckle denoising method based on iterative wavelet transform. This method uses multiple iterations to accurately estimate background noise and extract target information. Furthermore, it incorporates a bias threshold adjustment mechanism to more accurately distinguish between background and target areas, effectively removing noise while better preserving the image's structural features and details. This innovative method provides a new approach to solving key challenges in speckle denoising and has significant theoretical value and practical application prospects.

[0051] Furthermore, the present invention proposes an iterative wavelet transform speckle denoising method that can accurately remove the background information of the original speckle while retaining the Gaussian morphology unique to the original speckle, preserving the key feature information of the speckle to the greatest extent, laying the foundation for subsequent precise processing.

[0052] Furthermore, the proposed iterative wavelet transform-based speckle denoising method can accurately and effectively remove existing streak artifacts, effectively eliminating this critical factor affecting image quality. Simultaneously, the algorithm significantly improves image resolution and contrast. Through rigorous mathematical models and optimization strategies, it achieves higher clarity and fidelity in reconstructed images. Compared to traditional methods, the proposed algorithm better meets the stringent accuracy and clarity requirements of high-precision image restoration, providing a more reliable and efficient solution for the practical application of related technologies and significantly promoting technological development in this field.

[0053] Furthermore, the present invention introduces a deviation threshold mechanism in iterative wavelet analysis, which effectively avoids misjudging useful target information as background noise for removal, thereby ensuring the accuracy of the denoising process while retaining the effective information in the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 This is the flowchart of the speckle denoising proposed by the present invention;

[0056] Figure 2 This is a flow chart of the speckle denoising algorithm proposed in the present invention;

[0057] Figure 3 Schematic diagram of the imaging optical path and overall process of the embodiment;

[0058] Figure 4Comparison of the reconstruction results of the speckle denoising algorithm in the embodiment. DETAILED DESCRIPTION

[0059] The technical solution of the present invention will be described clearly and completely below. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0060] In order to solve the problem that speckle contains more random noise interference in scattered imaging scenes, resulting in poor quality of restored images, this paper proposes a speckle denoising method based on iterative wavelet transform to restore speckle with high quality. The specific steps are as follows, and the flow chart is as follows: Figure 1 As shown,

[0061] Step 1: Read the original speckle image

[0062] Read the original speckle image from the computer, denoted as I 0 ,The image contains random noise caused by imaging system noise and environmental interference;

[0063] Step 2: Initialize the deviation threshold

[0064] The initial deviation threshold T is set to the average pixel value of the input speckle image I0. In subsequent iterations, the threshold will be dynamically updated according to the pixel mean of the current input image;

[0065] Step 3: Multiscale wavelet decomposition

[0066] The input image is decomposed into n layers using a two-dimensional Daubechies-6 wavelet filter. Experiments show that 7 layers are the best, and the high-frequency coefficients of each layer {cD1, cD2, ..., cD n} and the lowest frequency coefficient cA n Only the lowest frequency coefficient cA is retained n , and all other high-frequency coefficients are set to zero. This decomposition process separates the image background noise into low-frequency components through the multi-resolution characteristics of wavelet transform;

[0067] Step 4: Inverse wavelet reconstruction background estimation

[0068] For the retained low-frequency coefficient cA n Perform inverse wavelet transform and reconstruct the background estimation image The mathematical expression is:

[0069]

[0070] Where IDWT represents inverse discrete wavelet transform, k represents the current number of iterations, is the lowest frequency coefficient cA retained in the kth iteration n ;

[0071] Step 5: Dynamic threshold correction, such as Figure 2 As shown,

[0072] The background estimation image Compare with the deviation threshold T set in step 2 and truncate pixels that exceed the threshold:

[0073]

[0074] By taking the result after threshold processing, the optimized residual image I is generated k ;

[0075] Step 6: Iterate and optimize the mechanism

[0076] Set the maximum number of iterations (experiments show that 5-7 times is the best effect), k represents the current number of iterations, if k <iterations:

[0077] The residual image I k As the input for the next round, return to step 2 to update the dynamic threshold;

[0078] If k = iterations, perform background estimation:

[0079]

[0080] The final denoised speckle image is equal to the original image minus the background estimate:

[0081]

[0082] Step 7: Deconvolution and reconstruction

[0083] After getting the denoised speckle Afterwards, the intensity distribution image O of the object can be transformed from the speckle Recover it from the image and get the denoised restored image to judge whether the method taken is effective:

[0084]

[0085] Where deconv(·) represents the deconvolution operation, PSF represents the point spread function of the system, and commonly used deconvolution algorithms include the Wiener filter algorithm, the regularized deconvolution algorithm, and the blind deconvolution algorithm.

[0086] 1. This paper proposes a speckle denoising method based on iterative wavelet transform. Unlike conventional denoising methods, this method uses multi-layer Daubechies-6 wavelet decomposition to accurately separate the low-frequency components of noise. Through multiple iterations, it achieves a progressive estimation of background noise, highlighting key features. Ultimately, the background noise is estimated and removed from the original signal, preserving the true characteristics of the signal to the greatest extent possible while robustly removing low-frequency noise. This method is unique in its overall architecture.

[0087] It should be noted that, in addition to wavelet transforms, other domain transform methods, such as the sine-cosine transform (DCT) and Fourier transform (DFT), also have good frequency domain separation capabilities and can effectively suppress noise within a similar framework. Therefore, the design concept of this invention has strong versatility, and the relevant transform methods can be adapted and replaced according to specific application requirements, further expanding the scope of application of this method in different types of image denoising tasks.

[0088] 2. The present invention introduces a dynamic threshold constraint mechanism in iterative wavelet analysis. During each round of iteration, the threshold is updated according to the input image, and the deviation points of the background estimate are checked based on the threshold. For areas that exceed the set threshold after each iteration, the threshold level is adjusted to the threshold level to avoid mistakenly removing useful target information as background noise, thereby ensuring the accuracy of denoising and the retention of effective information in the image. This is a key link to ensure good denoising effect.

[0089] In the speckle denoising method proposed in this paper, the iterative wavelet transform plays a key role. Its working mechanism is to accurately extract target information from speckle within the transform domain while effectively filtering out background information. This transform domain can also be replaced with the frequency domain, using Fourier transforms to similarly extract both high- and low-frequency information from speckle.

[0090] The deviation threshold set in the present invention is a soft threshold, that is, it changes with the number of iterations. If the threshold is set as a hard threshold, it can also replace the method of the present invention in certain specific cases.

[0091] Example 1

[0092] The imaging optical path and overall flow chart of a speckle denoising method based on iterative wavelet transform are as follows: Figure 3 As shown, where:

[0093] (a) is the imaged target "XDU"; (b) is the scattering medium, through which the target's light beam is scattered to form a speckle light field; (c) is the detector, which is used to receive the speckle pattern carrying target information; (d) is the original speckle directly collected by the detector, which contains a lot of background interference; (e) is the speckle obtained by speckle denoising method, which is the denoised speckle with noise removed; (f) is the point spread function (PSF) of the scattering medium collected by the detector; (g) is the clear restored image obtained by deconvolution of the processed speckle and the PSF.

[0094] The original speckle, the best existing Gaussian filter speckle denoising technology and the speckle denoising technology proposed in this invention are compared. The speckle patterns are as follows: Figure 4 (a), (b) and (c) are shown. The three speckles are deconvolved with the collected PSF to obtain the reconstructed images. Figure 4 (d)(g)(j), Figure 4 (e)(h)(k) and Figure 4 (f)(i)(l) As shown in FIG. 1 , it can be seen that the algorithm proposed in the present invention can effectively remove the stripe artifacts in the reconstructed image, and at the same time greatly improve the resolution and contrast of the reconstructed image.

[0095] In another embodiment of the present invention, a computer device is provided, comprising a processor and memory, the memory being configured to store a computer program, the computer program including program instructions, and the processor being configured to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It serves as the computing and control core of a terminal and is adapted to implement one or more instructions, specifically, to load and execute one or more instructions to implement a corresponding method flow or function. The processor described in this embodiment of the present invention can be used to implement a speckle denoising method based on iterative wavelet transform.

[0096] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device within a computer device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media within the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be high-speed RAM memory or non-volatile memory, such as at least one disk drive. The processor may load and execute the one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the speckle denoising method based on iterative wavelet transform in the above-described embodiment.

[0097] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A speckle denoising method based on iterative wavelet transform, characterized in that: include, Get the original speckle image I 0 , initialize the dynamic deviation threshold T, as the input speckle image I k The average pixel value of The input speckle image I k Perform multi-scale wavelet decomposition, retain the lowest frequency coefficients, and set the high frequency coefficients to 0; Perform inverse wavelet transform on the lowest frequency coefficient and reconstruct the background estimation image The background estimation image Compare with the deviation threshold T, truncate the pixels exceeding the threshold, and generate the residual image I k ; The residual image I k As the input of the next round, return to update the dynamic deviation threshold T until the preset number of iterations is reached, and use the final residual image as the background estimation Through the original speckle image I 0 Background subtraction estimation Obtain denoised speckle image Denoised speckle image Perform deconvolution operation to obtain the denoised restored image.

2. The speckle denoising method based on iterative wavelet transform according to claim 1, characterized in that: The original speckle image contains random noise caused by imaging system noise and environmental interference.

3. The speckle denoising method based on iterative wavelet transform according to claim 1, characterized in that: The input speckle image I k Perform multi-scale wavelet decomposition, retain the lowest frequency coefficients, and set the high frequency coefficients to 0, specifically: The input speckle image I is filtered using a two-dimensional Daubechies-6 wavelet filter. k Perform n-layer decomposition to obtain the high-frequency coefficients of each layer {cD1,cD2,...,cD n } and the lowest frequency coefficient cA n , only retain the lowest frequency coefficient cA n , and all other high-frequency coefficients are set to zero.

4. The speckle denoising method based on iterative wavelet transform according to claim 1, characterized in that: Perform inverse wavelet transform on the lowest frequency coefficient and reconstruct the background estimation image The expression is: Where IDWT represents inverse discrete wavelet transform, k represents the current number of iterations, is the lowest frequency coefficient cA retained in the kth iteration n .

5. The speckle denoising method based on iterative wavelet transform according to claim 1, characterized in that: The background estimation image Compare with the dynamic deviation threshold T and truncate the pixels that exceed the deviation threshold. The expression is: The lowest frequency coefficient cA n Compare it with the deviation threshold T, set the pixel value exceeding the deviation threshold T as the threshold T, and keep the pixel value below the deviation threshold T unchanged to obtain the final speckle image I k .

6. The speckle denoising method based on iterative wavelet transform according to claim 1, characterized in that: The residual image I k As the input for the next round, the updated dynamic deviation threshold T is returned until the preset number of iterations is reached, specifically: Set the maximum number of iterations, k represents the current number of iterations, If k < iterations, take the residual image I k as the input for the next round and return the updated deviation threshold T; If k = iterations, perform background estimation The expression is:

7. The speckle denoising method based on iterative wavelet transform according to claim 1, characterized in that: Denoised speckle image Perform deconvolution operation to obtain the denoised restored image. Where deconv(·) represents the deconvolution operation, and PSF represents the point spread function of the system.

8. A speckle denoising system based on iterative wavelet transform, characterized in that: include, Image acquisition module, used to obtain the original speckle image I 0 ; Dynamic threshold processing module, used to initialize the dynamic deviation threshold T, as the input speckle image I k The average pixel value of And the background estimation image Compare with the deviation threshold T, truncate the pixels exceeding the threshold, and generate the residual image I k ; The wavelet processing module is used to transform the input speckle image I k Perform multi-scale wavelet decomposition, retain the lowest frequency coefficients, and set the high frequency coefficients to 0; The reconstruction module is used to perform inverse wavelet transform on the lowest frequency coefficients to reconstruct the background estimation image Iterative optimization module is used to transform the residual image I k As the input of the next round, return to update the dynamic deviation threshold T until the preset number of iterations is reached, and use the final residual image as the background estimation Image restoration module, used to restore the original speckle image I 0 Background subtraction estimation Obtain denoised speckle image Denoised speckle image Perform deconvolution operation to obtain the target restored image.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the speckle denoising method based on iterative wavelet transform according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the speckle denoising method based on iterative wavelet transform according to any one of claims 1 to 7 are implemented.

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