Functional Analysis Debanding Method, System and Terminal Device for High-Resolution Images of Mars

Abstract

The present invention discloses a method, system and terminal device for strip removal by functional analysis of high-resolution Mars images, which relates to the technical field of planetary remote sensing image processing. The present invention includes: receiving high-resolution Mars image data containing strip noise as the original data input and performing preprocessing; constructing a degradation model for high-resolution images, performing wavelet decomposition on the original noisy image data based on the wavelet transform method, with the number of wavelet decomposition levels being n until no noise appears in each sub-image after decomposition. The present invention combines the advantages of multi-resolution of wavelet transform and automation in energy functional analysis, combines the advantages of the two methods, performs multi-layer wavelet decomposition on the original image, and then operates on the sub-images of the corresponding layers, removing strip noise to the greatest extent and retaining the spatial details of the ground objects to the greatest extent, and the denoised image has high fidelity.

Description

Technical Field

[0001] The present invention relates to the technical field of planetary remote sensing image processing, and in particular to a method, system and terminal device for strip removal by functional analysis of high-resolution Mars images. Background Art

[0002] At present, humans have carried out multiple deep space exploration missions on extraterrestrial celestial bodies such as the moon and Mars, and have made sufficient progress. High-resolution images obtained from Mars observation and imaging are of great scientific significance for the study of scientific issues such as the surface topography and geological evolution of Mars, and are also relatively scarce data resources in current Mars research. However, strip noise is a common degradation problem in remote sensing satellite imaging systems with multiple CCD mosaics. It not only obscures the true details of the surface, but also reduces the quality and application value of the image. Many factors can cause strip noise in the image, such as sensor aging, non-uniform response between CCD elements, temperature changes in the detector, mechanical movement, etc.

[0003] The principles of currently commonly used strip noise elimination for remote sensing images can be roughly summarized as: methods based on digital filtering, methods based on statistics, and methods based on optimization. The method of digital filtering is a commonly used and effective method for image denoising. Spatial domain filtering often loses image detail information. A common solution is to use a low-pass filter in the frequency domain to remove strip noise, achieving a good image denoising effect. However, most of the current methods for processing images in the frequency domain still have problems and deficiencies. Most filters cannot effectively separate the signal and the noise part, or it is very difficult to select the threshold and threshold function of wavelet transform. Moreover, the strip noise in high-resolution Mars remote sensing images often exists irregularly in the image. After selecting an inappropriate filter or threshold processing, blurring and ringing phenomena will occur, resulting in incomplete noise processing and misprocessing of effective information. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system and terminal device for strip removal by functional analysis of high-resolution Mars images, which reduces the traditional process of manual intervention, realizes the high fidelity of the image information after denoising, and at the same time improves the quality of the image after denoising.

[0005] According to the first aspect of the present invention, to achieve the above object, the present invention provides the following technical solution: A method for strip removal by functional analysis of high-resolution Mars images, comprising the following steps:

[0006] Receive high-resolution Mars image data containing strip noise as the original data input and perform preprocessing;

[0007] Construct a high - resolution image degradation model. Based on the wavelet transform method, perform wavelet decomposition on the original noisy image data. The number of wavelet decomposition levels is n, and stop until no noise appears in each sub - image after decomposition. Screen out the noisy sub - images with strip noise and filter out the sub - images without noise;

[0008] For all the noisy sub - images after wavelet decomposition, establish the energy functional of each noisy sub - image, construct a unidirectional total variation model for the noisy sub - image, impose a fidelity term and a regularization term, and use the alternating direction multiplier method to solve the minimum value of the functional. Set the tolerance to end the iterative convergence to obtain the processed noisy sub - image;

[0009] Use the inverse wavelet transform to reconstruct and restore the denoised sub - image and the sub - image without noise to obtain the entire denoised image;

[0010] Perform quantitative evaluation on the entire denoised image. If it does not meet the standard, repeat the denoising.

[0011] Furthermore, the high - resolution Mars image data with strip noise is obtained by satellite acquisition, and the original data is pre - processed, specifically including:

[0012] (21)Discriminate the image format to determine whether it is a single - band image or a multi - band image;

[0013] (22)If it is a multi - band image, process it band - by - band and finally synthesize one image;

[0014] (23)Discriminate the directionality of the strip noise in the image as horizontal, vertical or oblique.

[0015] Furthermore, construct a high - resolution image degradation model as follows:

[0016] For two - dimensional The high - resolution image degradation model with strip noise is expressed as:

[0017]

[0018] In the formula , represents the image with strip noise, represents the image without strip noise, represents the strip noise.

[0019] Furthermore, based on the wavelet transform method, perform wavelet decomposition on the original noisy image data. The number of wavelet decomposition levels is n, and stop until no noise appears in each sub - image after decomposition. Screen out the noisy sub - images with strip noise and filter out the sub - images without noise, specifically as follows:

[0020] For the original noisy image For it, the two-dimensional discrete wavelet transform is expressed as:

[0021]

[0022] Where is each subgraph component, is the decomposition scale, represents the domain space. Let the original noisy image be denoted as , then its multi-level decomposition is:

[0023]

[0024] In the formula and are and 's conjugate matrices, is the decomposition layer number, , , and respectively represent the low-frequency component, horizontal direction component, vertical direction component, and diagonal direction component after the -th layer decomposition.

[0025] Furthermore, for all the noisy subgraphs after wavelet decomposition, establish the energy functional of each noisy subgraph, construct a unidirectional total variation model for the noisy subgraph, apply the fidelity term and the regularization term, and use the alternating direction multiplier method to solve the minimum value of the functional. Set the tolerance to end the iterative convergence to obtain the processed noisy subgraph, specifically as follows:

[0026] (51) Establish the energy functional of each noisy subgraph, expressed as:

[0027]

[0028] In the formula is the fidelity term; is the regularization term;

[0029] (52) For the noisy image , its energy total variation model constructed in the bounded space is:

[0030]

[0031] In the formula is the fidelity term; is the regularization term, which is the total variation of , that is ; is the Lagrange factor, used to adjust the balance of the roles played by the fidelity term and the regularization term;

[0032] Therefore, image denoising becomes the problem of solving the energy functional minimization problem:

[0033]

[0034] where is the denoised image, and solving the minimum of the functional is converted into solving the Euler-Lagrange equation problem:

[0035]

[0036] In the formula, represents the image with strip noise, represents the image without strip noise, is the gradient of, is the Lagrange factor.

[0037] Furthermore, inverse wavelet transform is used to reconstruct and restore the processed denoised sub-image and the noise-free sub-image to obtain the entire denoised image, as follows:

[0038]

[0039] In the formula, and are and the conjugate matrices of, is the decomposition level, , , and respectively represent the low-frequency component, horizontal component, vertical component, and diagonal component after the decomposition of the th layer.

[0040] Furthermore, quantitative evaluation is performed on the entire denoised image, specifically using three quantitative indicators: peak signal-to-noise ratio, structural similarity, and relative dimensionless global comprehensive error, as follows:

[0041] (71) The calculation formula for the peak signal-to-noise ratio is as follows:

[0042]

[0043] In the formula, is the maximum gray value of the image, and are the number of rows and columns of the image pixels respectively, and are the th row and the The pixel values of the column;

[0044] (72) The structural similarity value ranges from 0 to 1, and the specific definition is as follows:

[0045]

[0046] Where, and are the average values of the two images and before and after denoising, and respectively represent the standard deviations of the two images before and after denoising, and are two positive integers;

[0047] (73) The relative dimensionless global comprehensive error, and the specific definition is as follows:

[0048]

[0049] Where, represents the ratio of the spatial resolution of the original image to the denoised image, represents the number of bands of the image, represents the root mean square error of the th band of the original image and the denoised image, is the mean value of the th band of the original image;

[0050] (74) Set SSIM≥0.95 and ERGAS≤10 as the ideal processing effect, otherwise perform repeated denoising.

[0051] According to the second aspect of the present invention, the present invention provides a Mars high-resolution image functional analysis stripe removal system for implementing the above-mentioned Mars high-resolution image functional analysis stripe removal method, including:

[0052] A preprocessing module for receiving the Mars high-resolution image data containing stripe noise as the original data input and performing preprocessing;

[0053] A construction module for constructing a high-resolution image degradation model, performing wavelet decomposition on the original noisy image data based on the wavelet transform method, with the number of wavelet decomposition layers being n, until no noise appears in each sub-image after decomposition, screening to obtain the noisy sub-images containing stripe noise and filtering out the sub-images without noise;

[0054] The noisy sub - image processing module is used to establish the energy functional of each noisy sub - image for all the noisy sub - images after wavelet decomposition, construct a unidirectional total variation model for the noisy sub - images, apply the fidelity term and the regularization term, solve the minimum value of the functional by the alternating direction method of multipliers, set the tolerance to end the iterative convergence, and obtain the processed noisy sub - images;

[0055] The image reconstruction module is used to reconstruct and restore the image by using the inverse wavelet transform for the denoised sub - images after processing and the sub - images without noise, and obtain the entire denoised image;

[0056] The evaluation module is used to quantitatively evaluate the entire denoised image. If it does not meet the standard, repeated denoising is performed.

[0057] According to the third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The computer program stored in the memory can run on the processor. When the processor loads and executes the computer program, the above - mentioned method for removing stripes from high - resolution Mars images by functional analysis is adopted.

[0058] According to the fourth aspect of the present invention, the present invention provides a storage medium containing computer - executable instructions, and the computer - executable instructions are used to execute the above - mentioned method for removing stripes from high - resolution Mars images by functional analysis when executed by a computer processor.

[0059] The present invention has at least the following beneficial effects:

[0060] (1) The present invention combines the advantages of wavelet transform with multi - resolution and automation in energy functional analysis, combines the advantages of the two methods, performs multi - layer wavelet decomposition on the original image, and then operates on the corresponding sub - images. It removes stripe noise to the greatest extent and retains the spatial details of the ground objects to the maximum extent. The fidelity of the denoised image is high;

[0061] (2) When the image restoration quality is comparable, the present invention minimizes the manual participation, reduces the process of setting parameters such as in traditional Fourier transform, reduces the intervention process and human intervention error factors, saves a large amount of manpower and material resources, and is suitable for automatically batch - processing a large amount of high - resolution Mars image data.

[0062] Of course, it is not necessary for any product implementing the present invention to achieve all the above - mentioned advantages simultaneously. Description of the Drawings

[0063] Figure 1 It is a schematic flow chart of the method described in the present invention;

[0064] Figure 2 It is a schematic principle diagram of the method described in the present invention;

[0065] Figure 3 Schematic diagram of three - layer wavelet decomposition in the embodiment of the present invention;

[0066] Figure 4 Schematic diagram of the direction of stripe - noise image in the embodiment of the present invention, where a is horizontal, b is vertical, and c is oblique;

[0067] Figure 5 Schematic diagram of HiRIC raw data in the embodiment of the present invention;

[0068] Figure 6 Schematic diagram of three - scale wavelet decomposition of HiRIC raw data in the embodiment of the present invention, where C1 represents the low - frequency sub - image, H1 represents the horizontal sub - image, V1 represents the vertical sub - image, and D1 represents the diagonal sub - image;

[0069] Figure 7 Gradient maps of HiRIC raw data in each direction in the embodiment of the present invention, where (a) is the gradient in the horizontal direction and (b) is the gradient in the vertical direction;

[0070] Figure 8 Schematic diagram of the image after stripe - noise removal in the embodiment of the present invention;

[0071] Figure 9 Comparison of the column means of the denoising result image and the original image (columns 250 - 400) of the present invention. Detailed implementation manners

[0072] Next, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present disclosure.

[0073] Please refer to Figure 1 and Figure 2 , the present invention provides a technical solution: a method for removing stripes from the functional analysis of high - resolution Mars images, including the following steps:

[0074] S1. Receive the high - resolution Mars image data containing stripe noise as the original data input (original high - resolution image), and perform pre - processing;

[0075] The high - resolution Mars image data containing stripe noise is acquired by satellite, and pre - processing is performed on the original data, specifically including:

[0076] (21) Determine whether the image format is a single - band image or a multi - band image;

[0077] If it is a multi - band image, it is processed band - by - band and finally synthesized into one image;

[0078] (23)Discriminate the directionality of the strip noise in the image as horizontal, vertical or oblique to prepare for subsequent operations;

[0079] As Figure 4 shown, the strip noise in the acquired image includes horizontal, vertical, and oblique, which are represented by a, b, and c respectively in Figure 4 ;

[0080] S2. Construct a high - resolution image degradation model. Based on the wavelet transform method, perform wavelet decomposition on the original noisy image data. The number of wavelet decomposition layers is n, and stop until no noise appears in each sub - image after decomposition. Screen out the noisy sub - images containing strip noise and filter out the sub - images without noise. The specific steps are as follows:

[0081] S21. Construct a high - resolution image degradation model, specifically as follows:

[0082] For a two - dimensional high - resolution image degradation model with strip noise, it is expressed as:

[0083]

[0084] In the formula , represents the image containing strip noise, represents the image without strip noise, represents the strip noise;

[0085] S22. Based on the wavelet transform method, perform wavelet decomposition on the original noisy image data. As Figure 3 shown, the specific steps are as follows:

[0086] For the original noisy image , perform two - dimensional discrete wavelet transform on it, which is expressed as:

[0087]

[0088] Among them are the components of each sub - layer, is the decomposition scale, represents the domain space. Assume that the original noisy image is denoted as , then its multi - layer decomposition is:

[0089]

[0090] In the formula and are and Conjugate matrix, is the decomposition level, , , and respectively represent the low-frequency component, horizontal component, vertical component, and diagonal component after the decomposition of the th layer;

[0091] S3. Consider an image as an energy system. For all the noisy sub-images after wavelet decomposition, establish the energy functional of each sub-image, construct a unidirectional total variational model (UTV) according to the characteristics of the directionality of strip noise, and only process in the noise direction without affecting the information in other directions. Place the regularization term under unidirectional variation mainly in the direction where the strips are located, and use the alternating direction multiplier method to solve the minimum value of the functional, setting the tolerance to end the iterative convergence and obtain the processed noisy sub-image;

[0092] S31. Establish the energy functional of each noisy sub-image, expressed as:

[0093]

[0094] where is the fidelity term, mainly used to constrain the denoised image to retain the original information as much as possible, is the regularization term, and its main role is to ensure that the image is smooth enough to remove the noise in the image;

[0095] (52) For the noisy image in the bounded space the constructed energy total variational model is:

[0096]

[0097] In the formula is the fidelity term; is the regularization term, which is the total variation of ; is the Lagrange factor, used to adjust the balance of the roles played by the fidelity term and the regularization term;

[0098] Therefore, image denoising becomes the problem of solving the minimum value of the energy functional :

[0099]

[0100] where For the denoised image, solving the minimum value of the functional is converted into solving the Euler-Lagrange equation problem:

[0101]

[0102] In the formula, represents the image containing stripe noise, represents the image without stripe noise, is 's gradient, is the Lagrange factor;

[0103] S4. Use the inverse wavelet transform to perform image reconstruction and restoration (wavelet reconstruction) on the processed denoised sub-image and the noise-free sub-image to obtain the entire denoised image, specifically as follows:

[0104]

[0105] In the formula, and are and 's conjugate matrices, is the decomposition level, , , and respectively represent the low-frequency component, horizontal direction component, vertical direction component, and diagonal direction component after the decomposition of the th layer;

[0106] S5. Conduct quantitative evaluation on the entire denoised image. If it does not meet the standard, repeat the denoising;

[0107] Three quantitative evaluation indicators, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Erreur Relative Globale Adimensionnelle de Synthese (ERGAS), are used for quantitative evaluation, specifically as follows:

[0108] S51. The Peak Signal to Noise Ratio (PSNR) is used to measure the ratio of the maximum signal to the noise intensity. The larger the value, the better the image restoration quality. Its calculation formula is as follows:

[0109]

[0110] In the formula, is the maximum gray value of the image, and are the number of rows and columns of the image pixels respectively, and are the pixel values of the th row and th column of the image before and after denoising respectively;

[0111] S52. The structural similarity SSIM considers that the image is structured, extracts features from brightness, contrast, and structure, and is used to measure the structural similarity between two images. The value ranges from 0 to 1. The larger the value, the better the quality of the denoised image and the more complete the original structural information retained. It is defined as follows:

[0112]

[0113] In the formula, and are the average values of the two images and before and after denoising, and represent the standard deviations of the two images before and after denoising respectively, and are two positive integers;

[0114] S53. The relative dimensionless global comprehensive error ERGAS can measure the global quality of the denoised image and can reflect the distortion of the image radiation value. Ideally, the value of ERGAS is 0, but in the real reconstructed image, its value is relatively high. The lower the value of ERGAS, the better the image quality. It is defined as follows:

[0115]

[0116] In the formula, represents the ratio of the spatial resolution of the original image to the denoised image, represents the number of image bands, represents the root mean square error of the th band of the original image and the denoised image, is the mean value of the th band of the original image;

[0117] S54. For high - resolution Mars images, set SSIM≥0.95 and ERGAS≤10 as the ideal processing effect. Otherwise, repeat steps S2 to S4 for re - denoising. Different image sources can be fine - tuned according to usage requirements.

[0118] The following is a detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Taking the image data obtained by the HiRIC high-resolution imaging camera of Tianwen-1 orbiter for imaging Mars as an example, this embodiment is carried out on the basis of the technical solution of the present invention and has achieved good results in the high-resolution image data of Tianwen-1 orbiter. While minimizing manual participation, it can effectively remove the strip noise in the high-resolution images of Mars and provide technical support for deep space exploration missions. This embodiment includes the following main steps:

[0119] Step 1: As shown in Figure 5 , obtain the HiRIC original image data. The image data is panchromatic band data. The experimental data selected in this embodiment is (400×400), the spatial resolution is about 0.7 m / pixel, the strip noise is obvious longitudinal strips, and the distribution is irregular. The most obvious strip is located at Figure 9 the 47th column of the original image in Figure 9 with a lower DN value (column mean) and a relatively black strip, and the 264th and 346th columns of the original image in

[0120] with higher DN values and relatively white strips; ) and the vertical subgraph ( ). Continuously decompose and observe. The noise disappears in the third-layer subgraph, and the decomposition stops. The number of wavelet decomposition layers is 3 layers. As shown in Figure 6 , where C1 represents the low-frequency subgraph, H1 represents the horizontal subgraph, V1 represents the vertical subgraph, and D1 represents the diagonal subgraph;

[0121] Step 3: Select all the noisy low-frequency subgraphs ( ) and the vertical subgraphs ( ), establish the energy system of the image, perform energy functional analysis, and use the alternating direction method of multipliers to solve the minimum value of the functional. Set the tolerance to end the iterative convergence. As shown in Figure 7 , where (a) represents the horizontal direction gradient and (b) represents the vertical direction gradient;

[0122] Step 4: Perform inverse wavelet transform on the processed subgraphs and the originally retained subgraphs to restore the image again. As shown in Figure 8 ;

[0123] Step 5: Calculate the quantitative evaluation index to evaluate the denoising result, as shown in Table 1:

[0124] Table 1 Quantitative Index Results

[0125]

[0126] As shown Figure 9 in the figure, it is a comparison of the column means of the denoised result image and the original image (columns 250 - 400).

[0127] The results show that the present invention has an obvious denoising effect and achieves good denoising results while ensuring that the information of the original image is not lost to the greatest extent.

[0128] Example Two:

[0129] This example provides a system for functional analysis and strip removal of high - resolution Mars images, which is used to implement the method for functional analysis and strip removal of high - resolution Mars images described in Example One, including:

[0130] A pre - processing module, which is used to receive the high - resolution Mars image data containing strip noise as the original data input and perform pre - processing;

[0131] A construction module, which is used to construct a degradation model for high - resolution images, perform wavelet decomposition on the original noisy image data based on the wavelet transform method, with the number of wavelet decomposition levels being n, until no noise appears in each sub - image after decomposition, screen out the noisy sub - images containing strip noise and filter out the sub - images without noise;

[0132] A noisy sub - image processing module, which is used to establish the energy functional of each noisy sub - image after wavelet decomposition, construct a unidirectional total variation model for the noisy sub - images, apply a fidelity term and a regularization term, solve the minimum value of the functional using the alternating direction method of multipliers, set a tolerance to end the iterative convergence, and obtain the processed noisy sub - images;

[0133] An image reconstruction module, which is used to perform image reconstruction and restoration on the processed denoised sub - images and the sub - images without noise using inverse wavelet transform to obtain the entire denoised image;

[0134] An evaluation module, which is used to perform quantitative evaluation on the entire denoised image, and if it does not meet the standard, perform repeated denoising.

[0135] Specifically, the above - mentioned pre - processing module, construction module, noisy sub - image processing module, image reconstruction module and evaluation module can be embedded in a computer processing system. The computer calls the above - mentioned modules to complete the task of fault diagnosis for the star catalog intelligent system according to the method for functional analysis and strip removal of high - resolution Mars images provided above; the above - mentioned pre - processing module, construction module, noisy sub - image processing module, image reconstruction module and evaluation module can perform operations according to the specific steps given by the method for functional analysis and strip removal of high - resolution Mars images.

[0136] It should be noted that it should be understood that the division of each module of the above system is only a division of logical functions. In actual implementation, it can be fully or partially integrated into a physical entity, or physically separated. And these modules can all be implemented in the form of software called by a processing element; they can also all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the preprocessing module can be a separately established processing element, or can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and the function of the above signal processing module can be called and executed by a certain processing element of the above device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together or can be independently implemented. The processing element mentioned here can be an integrated circuit with the ability to process signals. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuit of the hardware in the processor element or the instruction in the form of software.

[0137] For example, the above-mentioned modules can be one or more integrated circuits configured to implement the above method. For example: one or more Application Specific Integrated Circuits (ASICs), or, one or more Digital Signal Processors (DSPs), or, one or more Field Programmable Gate Arrays (FPGAs), etc. Again, when a certain module above is implemented in the form of a processing element scheduling program code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processors that can call program code. Again, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0138] Embodiment 3:

[0139] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor. When the processor loads and executes the computer program, the above-mentioned Mars high-resolution image functional analysis strip removal method is adopted.

[0140] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may further include input / output devices, network access devices, and a bus, etc.

[0141] Furthermore, the processor can be a central processing unit (CPU). Of course, according to the actual usage, 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. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc. This application does not make any restrictions in this regard.

[0142] Embodiment 4:

[0143] The present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the above-mentioned Mars high-resolution image functional analysis and stripe removal method when executed by a computer processor.

[0144] Among them, the computer program can be stored in a computer-readable medium. The computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some middleware form, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0145] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device.

[0146] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed to" or "disposed on" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0147] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0148] In the description of this specification, the description with reference to terms such as "an embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

Claims

1. A functional analysis method for removing banding from Mars high-resolution images, characterized in that: The following steps are involved: Receiving high-resolution Mars image data containing stripe noise as raw data input and performing preprocessing; A high-resolution image degradation model is constructed. The original noisy image data is decomposed by wavelet transform method. The number of wavelet decomposition layers is n. The decomposition is terminated when there is no noise in each sub-image after decomposition. The noisy sub-images containing stripe noise are screened out and the sub-images without noise are filtered out. For all noisy subgraphs after wavelet decomposition, the energy functional of each noisy subgraph is established. According to the directional characteristics of strip noise, a one-way total variation model is constructed. Only the strip noise direction is processed without affecting the information in other directions. The fidelity term and regularization term are applied. The regularization term under the one-way variation is placed in the direction where the strip is located. The alternating direction multiplier method is used to solve the functional minimum value. The tolerance is set to end the iterative convergence, and the processed noisy subgraph is obtained. The inverse wavelet transform is used to reconstruct and restore the processed denoised sub-image and the sub-image without noise to obtain the entire denoised image; Conduct quantitative evaluation on the entire denoised image, and repeat denoising if it does not meet the standard; Construct a high-resolution image degradation model as follows: The degradation model for a two-dimensional M×N high-resolution image with stripe noise is expressed as: g=z+s In the formula g represents an image with stripe noise, z represents an image without stripe noise, and s represents stripe noise.

2. The method for removing banding by functional analysis of high-resolution Mars images according to claim 1, characterized in that: The high-resolution image data of Mars containing stripe noise is acquired through satellite collection, and the raw data is preprocessed, including: (21) Determine whether the image format is a single-band image or a multi-band image; (22) If it is a multi-band image, it is processed band by band and finally synthesized into one image; (23) Determine whether the direction of the stripe noise in the image is horizontal, vertical or diagonal.

3. The method for removing banding by functional analysis of high-resolution Mars images according to claim 2, characterized in that: The original noisy image data is decomposed by wavelet transform method. The number of wavelet decomposition layers is n. The decomposition is terminated when there is no noise in each sub-image after decomposition. The noisy sub-image containing stripe noise is screened out and the sub-image without noise is filtered out. The details are as follows: For the original noisy image g(x,y), the two-dimensional discrete wavelet transform is expressed as: Among them, ω is the component of each sub-image, j is the number of decomposition layers, g(u,v) represents the domain space, and the original noisy image g(x,y) is recorded as C0, then its multi-layer decomposition is: Where G * and H * is the conjugate matrix of G and H, j is the number of decomposition levels, C j+1 , and They represent the low-frequency component, horizontal component, vertical component and diagonal component after decomposition of the j+1th layer respectively.

4. The method for removing banding by functional analysis of high-resolution Mars images according to claim 3, characterized in that: For all noisy subgraphs after wavelet decomposition, the energy functional of each noisy subgraph is established, and a one-way total variation model is constructed for the noisy subgraph. The fidelity term and regularization term are applied, and the alternating direction multiplier method is used to solve the functional minimum. The tolerance is set to end the iterative convergence, and the processed noisy subgraph is obtained, as follows: (51) The energy functional of each noisy subgraph is established and expressed as: E(f)=E1(f)+E2(f) Where E1(f) is the fidelity term; E2(f) is the regularization term; (52) For the noisy image g, the energy total variation model constructed in the bounded space Ω is: In the formula For the fidelity item; is the regularization term, which is the total variation of u, that is, λ is the Lagrangian factor, which is used to adjust the effect of the balance fidelity term and the regularization term; Therefore, image denoising becomes a problem of solving the minimum value of the energy functional E(u): in For the denoised image, solving the functional minimum is transformed into solving the Euler-Lagrange equation problem: In the formula, g represents an image with stripe noise, and u represents an image without stripe noise. is the gradient of u, and λ is the Lagrangian factor.

5. The method for removing banding by functional analysis of high-resolution Mars images according to claim 4, characterized in that: The inverse wavelet transform is used to reconstruct and restore the processed denoised sub-image and the noise-free sub-image to obtain the entire denoised image, as follows: In the formula, G * and H * is the conjugate matrix of H and J, j is the number of decomposition levels, C j , and They respectively represent the low-frequency component, horizontal component, vertical component and diagonal component after decomposition of the jth layer.

6. The method for removing banding by functional analysis of high-resolution Mars images according to claim 5, characterized in that: The entire denoised image is quantitatively evaluated using three quantitative indicators: peak signal-to-noise ratio, structural similarity, and relative dimensionless global comprehensive error, as shown below: (61) The peak signal-to-noise ratio calculation formula is as follows: Where MAX is the maximum grayscale value of the image, M and N are the number of rows and columns of image pixels, g(x,y) and u(x,y) are the pixel values ​​of the xth row and yth column of the image before and after denoising, respectively; (62) The structural similarity value ranges from 0 to 1 and is defined as follows: In the formula, μ g and μ u is the average value of the two images g and u before and after denoising, σ g and σ u Respectively represent the standard deviations of the two images before and after denoising, N1 and N2 are two positive integers; (63) Relative dimensionless global comprehensive error, specifically defined as follows: In the formula, represents the ratio of the spatial resolution of the original image to that of the denoised image, B represents the number of bands of the image, and RMSE(g i ,u i ) represents the root mean square error between the original image and the denoised image in the i-th band, MEAN(g i ) is the mean value of the i-th band of the original image; (64) Set SSIM ≥ 0.95 and ERGAS ≤ 10 for ideal processing effect, otherwise perform repeated denoising.

7. A Mars high-resolution image functional analysis and striping system, used to implement the Mars high-resolution image functional analysis and striping method according to any one of claims 1 to 6, characterized in that: include: A preprocessing module is used to receive high-resolution Mars image data containing stripe noise as raw data input and perform preprocessing; The construction module is used to construct a high-resolution image degradation model. The original noisy image data is decomposed by wavelet based on the wavelet transform method. The number of wavelet decomposition layers is n. The decomposition is terminated when there is no noise in each sub-image after decomposition. The noisy sub-image containing stripe noise is screened out and the sub-image without noise is filtered out. The noisy subgraph processing module is used to establish the energy functional of each noisy subgraph after wavelet decomposition, and construct a one-way total variation model for the noisy subgraph, apply the fidelity term and the regularization term, use the alternating direction multiplier method to solve the functional minimum, set the tolerance to end the iterative convergence, and obtain the processed noisy subgraph; An image reconstruction module is used to reconstruct and restore the processed denoised sub-image and the noise-free sub-image by using an inverse wavelet transform to obtain the entire denoised image; The evaluation module is used to perform quantitative evaluation on the entire denoised image, and repeat the denoising if it does not meet the standards.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the Mars high-resolution image functional analysis de-banding method described in any one of claims 1 to 6 is adopted.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the Mars high-resolution image functional analysis de-banding method according to any one of claims 1 to 6.

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