Snapshot spectral imaging reconstruction method and device, electronic equipment and storage medium

Abstract

The application provides a snapshot spectral imaging reconstruction method and device, electronic equipment and storage medium, and relates to the technical field of spectral imaging. The method comprises the following steps: constructing a first optimization equation based on two-dimensional image data, transmittance tensor and to-be-reconstructed spectral data, wherein the first optimization equation comprises a data fidelity term, a group sparse total variation term in the spatial dimension and a total variation term in the spectral dimension; introducing target auxiliary variables and target dual variables of the spectral data; and solving the target auxiliary variables and the target dual variables in a second optimization equation to solve the spectral data. By introducing the group coefficient total variation term in the spatial dimension and the total variation term in the spectral dimension, the application can enhance the smoothness and continuity of the spatial structure of the reconstructed spectral data, and the smoothness of the spectral data in the spectral dimension, effectively avoid the difference of the spectral data at the edge of the target scene, further avoid color artifacts, and improve the quality of the reconstructed spectral data.

Description

Technical Field

[0001] This application relates to the field of spectral imaging technology, and in particular to a snapshot spectral imaging reconstruction method, apparatus, electronic device and storage medium. Background Technology

[0002] Snapshot spectral imaging technology records coded spectral data from a single exposure using a two-dimensional detector, and then reconstructs the original three-dimensional hyperspectral cube using reconstruction algorithms. Snapshot spectral imaging typically maps high-dimensional spectral data into intuitive pseudo-color images to improve information interpretation efficiency. However, the quality of pseudo-color images is highly dependent on the spatial consistency of the reconstructed spectral bands. Edge misalignment between bands manifests as color artifacts (chromatic aberration) in the synthesized image, compromising the visual realism and reliability of the analysis. Therefore, it is necessary to restore the original spectral image with high fidelity during the reconstruction of undersampled data and ensure strict alignment of the edges of each band.

[0003] Existing snapshot spectral reconstruction algorithms mainly rely on compressed sensing methods based on physical models. Compared to deep learning methods, model-based methods construct regularization constraints by introducing prior knowledge of spectral images, transforming reconstruction into an interpretable optimization problem.

[0004] Existing total variation regularization reconstruction techniques process each spectral band independently due to the anisotropic spatial total variation regularization term. Consequently, at the same pixel location, different gradient values ​​experience varying degrees of shrinkage. This independent shrinkage mechanism disrupts the spatial correlation between bands, resulting in significant differences and misalignments between the reconstructed 2D spatial images of different bands at the edges of the target scene, leading to poor final spectral imaging quality. Summary of the Invention

[0005] This application provides a snapshot spectral imaging reconstruction method, apparatus, electronic device, and storage medium to address the shortcomings of poor snapshot spectral imaging quality in the prior art and improve the quality of snapshot spectral imaging.

[0006] This application provides a snapshot spectral imaging reconstruction method, including: Based on the acquired two-dimensional image data from snapshot spectral imaging, the transmittance tensor of the multilayer thin-film array, and the spectral data to be reconstructed, a first optimization equation is constructed. This first optimization equation includes a data fidelity term and a term based on... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixing norm; By introducing the target auxiliary variable and the target dual variable of spectral data into the first optimization equation, the second optimization equation is obtained. In the second optimization equation, the target auxiliary variable and the target dual variable are solved to obtain the spectral data.

[0007] According to the snapshot spectral imaging reconstruction method provided in this application, the target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data, including: In the second optimization equation, the solution of the (k+1)th spectral data is calculated based on the kth objective auxiliary variable solution and the kth objective dual variable solution; Based on the (k+1)th spectral data solution and the kth objective dual variable solution, calculate the (k+1)th objective auxiliary variable solution; Based on the (k+1)th objective auxiliary variable solution, the (k+1)th spectral data solution, and the (k)th objective dual variable solution, calculate the (k+1)th objective dual variable solution; Update k+1 to k, and iteratively solve the (k+1)th spectral data solution based on the updated k-th objective auxiliary variable solution and the updated k-th objective dual variable solution until the residual of the second optimization equation is less than the set residual; the first objective auxiliary variable solution is the initial auxiliary variable; the first objective dual variable solution is the initial dual variable, k≥1; The spectral data solution obtained from the last calculation is used as the spectral data.

[0008] According to the snapshot spectral imaging reconstruction method provided in this application, the target auxiliary variables include horizontal auxiliary variables, vertical auxiliary variables, and spectral auxiliary variables, which are determined based on the following method: Based on the horizontal spatial difference operator matrix and spectral data, determine the horizontal auxiliary variables; Based on the vertical spatial difference operator matrix and spectral data, determine the vertical auxiliary variables; Based on the spectral difference operator matrix and spectral data, spectral auxiliary variables are determined.

[0009] According to the snapshot spectral imaging reconstruction method provided in this application, the k-th target dual variable solution includes the k-th level dual variable solution, and the (k+1)-th target auxiliary variable solution includes the (k+1)-th level auxiliary variable solution. The (k+1)-th level auxiliary variable solution is calculated based on the following method: Based on the (k+1)th spectral data solution, the horizontal spatial difference operator matrix, and the kth horizontal dual variable solution, the horizontally corrected spectral vector is determined. Substitute the horizontally corrected spectral vector into Solving the equations for the proximal operators of the norm yields the solution for the (k+1)th level auxiliary variable.

[0010] According to the snapshot spectral imaging reconstruction method provided in this application, the k-th target dual variable solution includes the k-th vertical dual variable solution, and the (k+1)-th target auxiliary variable solution includes the (k+1)-th vertical auxiliary variable solution. The (k+1)-th vertical auxiliary variable solution is calculated based on the following method: Based on the (k+1)th spectral data solution, the vertical spatial difference operator matrix, and the kth vertical dual variable solution, the vertically corrected spectral vector is determined. Substitute the vertically corrected spectral vector Solving the equations for the proximal operators of the norm yields the (k+1)th solution with vertical auxiliary variables.

[0011] According to the snapshot spectral imaging reconstruction method provided in this application, the k-th target dual variable solution includes the k-th spectral dual variable solution, and the (k+1)-th target auxiliary variable solution includes the (k+1)-th spectral auxiliary variable solution. The (k+1)-th spectral auxiliary variable solution is calculated based on the following method: Based on the (k+1)th spectral data solution, the spectral difference operator matrix, and the kth spectral dual variable solution, the spectral vector after spectral correction is determined. Substituting the spectral vector after spectral correction into the solution equation of the scalar soft threshold operator, we obtain the solution of the spectral auxiliary variable for the (k+1)th iteration.

[0012] According to the snapshot spectral imaging reconstruction method provided in this application, the residuals of the second optimization equation include the original residuals and the dual residuals, which are determined based on the following method: The original residuals are determined based on the (k+1)th objective auxiliary variable solution and the (k+1)th spectral data solution. The dual residual is determined based on the k-th objective auxiliary variable solution and the (k+1)-th objective auxiliary variable solution.

[0013] This application also provides a snapshot spectral imaging reconstruction apparatus, comprising: An optimization equation construction module is used to construct a first optimization equation based on the acquired two-dimensional image data of snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term, a group sparse total variation term in the spatial dimension, and a total variation term in the spectral dimension. An optimization equation construction module is used to introduce target auxiliary variables and target dual variables of spectral data into the first optimization equation to obtain the second optimization equation; The solver module is used to solve for the target auxiliary variable and the target dual variable in the second optimization equation to solve for the spectral data.

[0014] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the snapshot spectral imaging reconstruction method as described above.

[0015] This application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the snapshot spectral imaging reconstruction method as described above.

[0016] The snapshot spectral imaging reconstruction method, apparatus, electronic device, and storage medium provided in this application construct a first optimization equation based on the acquired two-dimensional image data of the snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixture norm is used; a target auxiliary variable and a target dual variable of spectral data are introduced into the first optimization equation to obtain the second optimization equation; the target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data. This application introduces a method based on... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixture norm can enhance the smoothness and coherence of the spatial structure of the reconstructed spectral data, as well as the smoothness of the spectral data in the spectral dimension. It can effectively avoid the differences in spectral data at the edge of the target scene, further avoid color artifacts (chromatic aberration), and improve the quality of the reconstructed spectral data. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts of the snapshot spectral imaging reconstruction method provided in this application.

[0019] Figure 2 This is the second schematic diagram of the snapshot spectral imaging reconstruction method provided in this application.

[0020] Figure 3 This is a schematic diagram of the snapshot spectral imaging reconstruction device provided in this application.

[0021] Figure 4This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Snapshot spectral imaging reconstruction can be transformed into an interpretable optimization problem by incorporating prior knowledge of the spectral image (e.g., smoothness, sparsity, etc.) to construct regularization constraints, such as total variation, low rank, and second derivative. Among these, total variation regularization is widely used because it effectively preserves edges and suppresses noise.

[0024] Total variational regularization (TV) was initially applied to image denoising, effectively suppressing noise while preserving edge structure and avoiding edge blurring. With the widespread application of snapshot compression imaging technology in the spectral and video fields, numerous studies have transformed the reconstruction decoding problem into a solution model for denoising images across each channel. Within this framework, TV has been widely used to eliminate degradation caused by encoding and reconstruct edges in images across different bands. Mainstream reconstruction algorithms include the Alternating Direction Method of Multipliers – Total Variation (ADMM-TV), the Two-Step Iterative Shrinkage / Thresholding – Total Variation (TwIST-TV), and the Generalized Alternating Projection – Total Variation (GAP-TV), among others.

[0025] Snapshot-style spectral imaging systems construct regularization terms incorporating prior knowledge by reconstructing the sparse coefficients of the total variation domain of spectral data. The sparse coefficients of the total variation domain include total variation terms in both the spatial and spectral dimensions. Leveraging the prior knowledge that the target scene information is mostly smooth in the spatial dimension and that spectra in nature are smooth, the sparse coefficients of the total variation domain achieve the reconstruction of two-dimensional images for each spectral channel and spectral smoothing constraints. The calculation formula for the regularization term incorporating prior knowledge is as follows.

[0026] (1); in, For regularization terms that include prior knowledge, For the total variation of the two-dimensional space dimension, For the total variation term in the spectral dimension.

[0027] The formula for calculating the total variation term of the spatial dimension is as follows.

[0028] (2); in, For the total variation of the spatial dimension, For the total variation weights of the spatial dimension, For spectral data The Middle Spatial images of individual bands The pixel spectral intensity amplitude at the location, For horizontal difference operators, For vertical difference operators, In order to seek Norm, The horizontal space difference operator matrix, is the vertical space difference operator matrix.

[0029] According to formula (2), the total variation term of the spatial dimension of the anisotropic two-dimensional system is processed independently for each band, and the two-dimensional image of each independent band is realized in the gradient domain coefficients only in each iteration. Norm minimization The proximal operator of the norm is a scalar soft-thresholding operator. This operator performs soft-thresholding shrinkage independently on the gradient of each band. Different gradient values ​​will be shrunk to different degrees. This leads to differences in the target scene edges reconstructed from the two-dimensional spatial images of the same pixel location in different bands. This will result in color artifacts (chromatic aberration) and poor final spectral imaging quality.

[0030] To address color artifacts, this application proposes a novel snapshot spectral imaging reconstruction method based on group sparse total variation. This method introduces cross-band... The mixture norm is used to solve the problem, which constrains all spectral bands to share a consistent spatial edge structure, thereby suppressing edge color artifacts caused by inconsistencies in the edges of different bands at the source.

[0031] Figure 1 This is one of the flowcharts of the snapshot spectral imaging reconstruction method provided in this application, such as... Figure 1 As shown, the snapshot spectral imaging reconstruction method includes steps S100 to S300, and the specific steps are as follows.

[0032] S100: Based on the acquired two-dimensional image data from snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed, the first optimization equation is constructed.

[0033] The first optimization equation includes data fidelity terms, based on... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixing norm.

[0034] Two-dimensional image data of snapshot spectral imaging is acquired through a snapshot spectral imaging device. The snapshot spectral imaging device includes a snapshot spectral camera, and its core optical components typically include an imaging lens group, a multilayer thin film array, and a grayscale detector.

[0035] An imaging lens group captures three-dimensional spectral data of the target scene. A multilayer thin-film array encodes the spectral data, and a grayscale detector captures the encoded spectral data to obtain two-dimensional image data (e.g., a two-dimensional mosaic image). The compressed sensing process of spectral data through the multilayer thin-film array can be represented by the following formula.

[0036] (3); in, It is two-dimensional image data. For the transmittance tensor of a multilayer thin film array and spectral data exist The bands are multiplied element by element. , ,in, and For spatial dimensions, This represents the number of spectral bands.

[0037] Given a forward model, the goal of spectral image reconstruction is to reconstruct spectral images from measured two-dimensional image data. Recover the spectral data. The computation of spectral data can be transformed into the following optimization problem.

[0038] (4); in, It is the Frobenius norm. It is two-dimensional image data. For the transmittance tensor of a multilayer thin film array and spectral data exist The bands are multiplied element by element. This represents the number of spectral bands. This is a regularization term that incorporates prior knowledge and includes regularization parameters.

[0039] To achieve cross-band edge alignment, this application utilizes the novel physical prior knowledge that "in nature, the spatial boundary of an object is determined by the distribution of matter, and the boundary of matter should appear in the same position across all spectral bands." By improving the total variational term of the spatial dimension of anisotropy, this application adopts a method based on... The group sparse total variation term of the mixing norm is used to replace the term based on... The total variational terms of the norm for each band of the spatial dimension are independent. The formula for calculating the grouped sparse total variational terms of the spatial dimension is as follows.

[0040] (5); in, For the group of sparse total variational terms in the spatial dimension, For the spatial dimension, group sparse total variation weights In order to seek Norm, For horizontal difference operators, For vertical difference operators, The horizontal space difference operator matrix, The matrix represents the vertical space difference operator. For spectral data The Middle Spatial images of individual bands The pixel spectral intensity amplitude at the location, In order to seek Norm, for The gradient vector of each band at the location in the horizontal direction. for The gradient vector of each band at the location in the vertical direction.

[0041] The difference between Formula (5) and Formula (2) is that the total variation (Formula (2)) term for the independent spatial dimension is first applied within each band. The norm is then summed across bands. The group sparse total variational term of the spatial dimension used in this application (Formula (5)) is first calculated across bands at each spatial location. Norms, and then applied across spatial locations Norm. This is equivalent to applying a cooperative constraint between bands in the gradient domain, forcing all bands to share similar edge responses at the same locations.

[0042] because The norm is more sensitive to larger gradient values. This regularization term (a group of sparse total variational terms in the spatial dimension) preferentially aligns edges that are prominent across multiple bands, while suppressing isolated gradient values ​​that appear only in a few bands (often corresponding to noise or artifacts). This helps improve cross-band edge alignment, enhances the edge consistency of the reconstructed spectral image, reduces color artifacts caused by inter-band edge misalignment, and strengthens the smoothness and coherence of the spatial structure, especially in regions with complex textures or rich edges.

[0043] The regularization term in this application that includes prior knowledge Including based on The grouped sparse total variational terms of the spatial dimension of the mixture norm, and based on The total variational term of the spectral dimension of the mixing norm. Based on the acquired two-dimensional image data from snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed, the first optimization equation is constructed. The final calculation formula of the first optimization equation is as follows.

[0044] (6); in, For data fidelity items, For the group of sparse total variational terms in the spatial dimension, For the total variation term in the spectral dimension, The spectral difference operator matrix is... It is two-dimensional image data. For the transmittance tensor of a multilayer thin film array and spectral data exist The bands are multiplied element by element. For the total variation weights in the spectral dimension, For the spatial dimension, group sparse total variation weights In order to seek Mixed norm, The horizontal space difference operator matrix, The matrix represents the vertical space difference operator. In order to seek Norm, This represents the number of spectral bands.

[0045] The spatial dimension sparse total variation term is used to enhance the smoothness and coherence of the spatial structure of spectral data. The spectral dimension total variation term is used to constrain the smoothness along the spectral dimension.

[0046] S200: Introduce the target auxiliary variable and target dual variable of spectral data into the first optimization equation to obtain the second optimization equation.

[0047] This application employs the alternating direction multiplier method to solve the optimization equation. First, an objective auxiliary variable is introduced to decouple the optimization equation from its constraints. Then, the optimization equation is transformed into an augmented Lagrangian function, yielding the objective dual variable.

[0048] By introducing the target auxiliary variable and target dual variable of spectral data into the first optimization equation, the second optimization equation is obtained.

[0049] The purpose of introducing the target auxiliary variable and the target dual variable in this application is to simplify the solution process of the first optimization equation.

[0050] S300: Solve for the target auxiliary variable and the target dual variable in the second optimization equation to obtain the spectral data.

[0051] In the second optimization equation, the target auxiliary variable and the target dual variable are solved to obtain the spectral data.

[0052] The snapshot spectral imaging reconstruction method provided in this application constructs a first optimization equation based on the acquired two-dimensional image data of the snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a term based on… Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixture norm is used; a target auxiliary variable and a target dual variable of spectral data are introduced into the first optimization equation to obtain the second optimization equation; the target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data. This application introduces a method based on... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixture norm can enhance the smoothness and coherence of the spatial structure of the reconstructed spectral data, as well as the smoothness of the spectral data in the spectral dimension. It can effectively avoid the differences in spectral data at the edge of the target scene, further avoid color artifacts, and improve the quality of the reconstructed spectral data.

[0053] Based on the above embodiments, the target auxiliary variables include horizontal auxiliary variables, vertical auxiliary variables, and spectral auxiliary variables, and the target auxiliary variables are determined in the following manner: Based on the horizontal spatial difference operator matrix and spectral data, determine the horizontal auxiliary variables; Based on the vertical spatial difference operator matrix and spectral data, determine the vertical auxiliary variables; Based on the spectral difference operator matrix and spectral data, spectral auxiliary variables are determined.

[0054] Furthermore, the target dual variables include horizontal dual variables, vertical dual variables, and spectral dual variables. By introducing the target auxiliary variables and target dual variables of spectral data into the first optimization equation, the second optimization equation is obtained. The calculation formula for the second optimization equation is as follows.

[0055] (7); in, For data fidelity terms after decoupling constraints, For data fidelity items, It is two-dimensional image data. For spectral data, As a level auxiliary variable, As a vertical auxiliary variable, As a spectral auxiliary variable, The horizontal space difference operator matrix, The matrix represents the vertical space difference operator. The spectral difference operator matrix is... For the total variation weights in the spectral dimension, For the spatial dimension, group sparse total variation weights In order to seek Mixed norm, For vertical dual variables, For horizontal dual variables, For spectral dual variables, For penalty parameters, To augment the Lagrange function.

[0056] This application can decouple the group sparse total variation terms of the spatial dimension in the first optimization equation by introducing horizontal and vertical auxiliary variables, and can decouple the total variation terms of the spectral dimension in the first optimization equation by introducing spectral auxiliary variables.

[0057] Based on the above embodiments, the objective auxiliary variable and the objective dual variable are solved in the second optimization equation to solve for the spectral data, including the following steps: In the second optimization equation, the solution of the (k+1)th spectral data is calculated based on the kth objective auxiliary variable solution and the kth objective dual variable solution; Based on the (k+1)th spectral data solution and the kth objective dual variable solution, calculate the (k+1)th objective auxiliary variable solution; Based on the (k+1)th objective auxiliary variable solution, the (k+1)th spectral data solution, and the (k)th objective dual variable solution, calculate the (k+1)th objective dual variable solution; Update k+1 to k, and iteratively solve the (k+1)th spectral data solution based on the updated k-th objective auxiliary variable solution and the updated k-th objective dual variable solution until the residual of the second optimization equation is less than the set residual; the first objective auxiliary variable solution is the initial auxiliary variable; the first objective dual variable solution is the initial dual variable, k≥1; The spectral data solution obtained from the last calculation is used as the spectral data.

[0058] In the second optimization equation, the (k+1)th spectral data solution is calculated based on the k-th objective auxiliary variable solution and the k-th objective dual variable solution. Here, the 1st objective auxiliary variable solution is the initial auxiliary variable; the 1st objective dual variable solution is the initial dual variable, and k≥1.

[0059] The initial auxiliary variables include the initial horizontal auxiliary variable, the initial vertical auxiliary variable, and the initial spectral auxiliary variable.

[0060] The initial dual variables include the initial vertical dual variable, the initial horizontal dual variable, and the initial spectral dual variable.

[0061] The k-th objective auxiliary variable solution includes the k-th horizontal auxiliary variable solution, the k-th vertical auxiliary variable solution, and the k-th spectral auxiliary variable solution. The (k+1)-th objective auxiliary variable solution includes the (k+1)-th horizontal auxiliary variable solution, the (k+1)-th vertical auxiliary variable solution, and the (k+1)-th spectral auxiliary variable solution.

[0062] The k-th objective dual variable solution includes the k-th horizontal dual variable solution, the k-th vertical dual variable solution, and the k-th spectral dual variable solution. The (k+1)-th objective dual variable solution includes the (k+1)-th horizontal dual variable solution, the (k+1)-th vertical dual variable solution, and the (k+1)-th spectral dual variable solution.

[0063] In the second optimization equation, the (k+1)th spectral data solution is calculated based on the k-th objective auxiliary variable solution and the k-th objective dual variable solution. Specifically, the (k+1)th spectral data solution is calculated based on the k-th horizontal auxiliary variable solution, the k-th vertical auxiliary variable solution, the k-th spectral auxiliary variable solution, the k-th horizontal dual variable solution, the k-th vertical dual variable solution, and the k-th spectral dual variable solution. The formula for calculating the (k+1)th spectral data solution is as follows.

[0064] (8); in, For data fidelity items, It is two-dimensional image data. For the spectral data that needs to be solved, For the solution of the auxiliary variable at the k-th level, For the k-th vertical auxiliary variable solution, For the k-th spectral auxiliary variable solution, The horizontal space difference operator matrix, The matrix represents the vertical space difference operator. The spectral difference operator matrix is... For the k-th vertical dual variable solution, For the k-th level dual variable solution, For the k-th spectral dual variable solution, For penalty parameters, This is the solution for the (k+1)th spectral data.

[0065] Based on the (k+1)th spectral data solution and the kth objective dual variable solution, the (k+1)th objective auxiliary variable solution is calculated, including the following cases.

[0066] (1) Calculate the solution of the auxiliary variable at level (k+1) based on the solution of the spectral data of level (k+1) and the solution of the dual variable at level (k). The formula for calculating the solution of the auxiliary variable at level (k+1) is as follows.

[0067] (9); in, For the (k+1)th level auxiliary variable solution, For the level auxiliary variable solution that needs to be solved, For penalty parameters, The horizontal space difference operator matrix, For the k-th level dual variable solution, In order to seek Mixed norm, For the (k+1)th spectral data solution, For the spatial dimension, there are grouped sparse total variation weights.

[0068] (2) Calculate the (k+1)th vertical auxiliary variable solution based on the (k+1)th spectral data solution and the (k)th vertical dual variable solution. The formula for calculating the (k+1)th vertical auxiliary variable solution is as follows.

[0069] (10); in, For the (k+1)th vertical auxiliary variable solution, For the solution of the vertical auxiliary variable that needs to be solved, For penalty parameters, The matrix represents the vertical space difference operator. For the k-th vertical dual variable solution, In order to seek Mixed norm, For the (k+1)th spectral data solution, For the spatial dimension, there are grouped sparse total variation weights.

[0070] (3) Calculate the auxiliary variable solution of the (k+1)th spectral data based on the solution of the (k+1)th spectral data and the dual variable solution of the (k)th spectral data. The formula for calculating the auxiliary variable solution of the (k+1)th spectral data is as follows.

[0071] (11); in, For the (k+1)th spectral auxiliary variable solution, For the solution of the spectral auxiliary variable that needs to be solved, For penalty parameters, The spectral difference operator matrix is... For the k-th spectral dual variable solution, In order to seek Norm, For the (k+1)th spectral data solution, For the spectral dimension, the total variation weights are used.

[0072] Based on the (k+1)th objective auxiliary variable solution, the (k+1)th spectral data solution, and the (k)th objective dual variable solution, the (k+1)th objective dual variable solution is calculated, which includes the following cases.

[0073] (1) Calculate the solution of the dual variable at level (k+1) based on the solution of the auxiliary variable at level (k+1), the solution of the spectral data at level (k+1), and the solution of the dual variable at level (k).

[0074] (2) Based on the (k+1)th vertical auxiliary variable solution, the (k+1)th spectral data solution and the (k)th vertical dual variable solution, calculate the (k+1)th vertical dual variable solution.

[0075] (3) Calculate the dual variable solution of the spectrum based on the auxiliary variable solution of the (k+1)th spectrum, the data solution of the (k+1)th spectrum, and the dual variable solution of the (k)th spectrum.

[0076] The formulas for calculating the (k+1)th horizontal dual variable solution, the (k+1)th vertical dual variable solution, and the (k+1)th spectral dual variable solution are as follows.

[0077] (12); in, For the (k+1)th level dual variable solution, For the (k+1)th vertical dual variable solution, For the (k+1)th spectral dual variable solution, For the (k+1)th level auxiliary variable solution, For the (k+1)th vertical auxiliary variable solution, For the (k+1)th spectral auxiliary variable solution, The horizontal space difference operator matrix, The matrix represents the vertical space difference operator. The spectral difference operator matrix is... For the k-th level dual variable solution, For the k-th vertical dual variable solution, For the k-th spectral dual variable solution, This is the solution for the (k+1)th spectral data.

[0078] Update k+1 to k, use the calculated (k+1)th objective auxiliary variable solution as the updated (k)th objective auxiliary variable solution, and use the calculated (k+1)th objective dual variable solution as the updated (k)th objective dual variable solution.

[0079] The updated k-th objective auxiliary variable solution includes the updated k-th horizontal auxiliary variable solution, the updated k-th vertical auxiliary variable solution, and the updated k-th spectral auxiliary variable solution.

[0080] The updated k-th objective dual variable solution includes the updated k-th horizontal dual variable solution, the updated k-th vertical dual variable solution, and the updated k-th spectral dual variable solution.

[0081] Based on the updated k-th objective auxiliary variable solution and the updated k-th objective dual variable solution, iteratively solve the (k+1)-th spectral data solution until the residual of the second optimization equation is less than the set residual.

[0082] This application utilizes the alternating direction multiplier method to iteratively solve for the target auxiliary variable, the target dual variable, and the spectral data. Through iterative solving, this application gradually approximates the accurate spectral data.

[0083] Based on the above embodiments, the k-th objective dual variable solution includes the k-th level dual variable solution, and the (k+1)-th objective auxiliary variable solution includes the (k+1)-th level auxiliary variable solution. The (k+1)-th level auxiliary variable solution is calculated in the following manner: Based on the (k+1)th spectral data solution, the horizontal spatial difference operator matrix, and the kth horizontal dual variable solution, the horizontally corrected spectral vector is determined. Substitute the horizontally corrected spectral vector into Solving the equations for the proximal operators of the norm yields the solution for the (k+1)th level auxiliary variable.

[0084] The formula for calculating the solution of the auxiliary variable at level k+1 is as follows.

[0085] (13); in, For level auxiliary variables in Spectral vector at position, For the (k+1)th spectral data solution, This is the spectral vector after horizontal correction. For the (k+1)th spectral data solution, For the spatial dimension, group sparse total variation weights For the (k+1)th level auxiliary variable solution, For the level auxiliary variable solution that needs to be solved, For penalty parameters, The horizontal space difference operator matrix, For the k-th level dual variable solution, In order to seek Norm, For the (k+1)th spectral data solution, In order to seek Norm, For the spatial dimension, group sparse total variation weights for The equation for solving the proximal operators of the norm.

[0086] This application substitutes the horizontally corrected spectral vector into... The equations for solving the proximal operators of the norm enable accurate calculation of the (k+1)th level auxiliary variable solution.

[0087] Based on the above embodiments, the k-th objective dual variable solution includes the k-th vertical dual variable solution, and the (k+1)-th objective auxiliary variable solution includes the (k+1)-th vertical auxiliary variable solution. The (k+1)-th vertical auxiliary variable solution is calculated in the following manner: Based on the (k+1)th spectral data solution, the vertical spatial difference operator matrix, and the kth vertical dual variable solution, the vertically corrected spectral vector is determined. Substitute the vertically corrected spectral vector Solving the equations for the proximal operators of the norm yields the (k+1)th solution with vertical auxiliary variables.

[0088] The formula for calculating the (k+1)th vertical auxiliary variable solution is as follows.

[0089] (14); in, For vertical auxiliary variables in Spectral vector at position, For the (k+1)th spectral data solution, This is the spectral vector corrected in the vertical direction. For the (k+1)th spectral data solution, For the spatial dimension, group sparse total variation weights For the (k+1)th vertical auxiliary variable solution, For the solution of the vertical auxiliary variable that needs to be solved, For penalty parameters, The matrix represents the vertical space difference operator. For the k-th vertical dual variable solution, In order to seek Norm, For the (k+1)th spectral data solution, In order to seek Norm, For the spatial dimension, group sparse total variation weights for The equation for solving the proximal operators of the norm.

[0090] This application substitutes the vertically corrected spectral vector into... The equations for solving the proximal operators of the norm enable accurate calculation of the (k+1)th vertical auxiliary variable solution.

[0091] The solution equation for the proximal operator of the norm is obtained by using the vector soft thresholding operator. as well as A uniform scaling factor is applied to all band components, a characteristic that ensures edge consistency across bands: when the gradient magnitude at a spatial location is sufficiently large, the edges of all bands are preserved synchronously; conversely, they are removed synchronously. Therefore, edges will not exist only in some bands, effectively suppressing color artifacts.

[0092] Based on the above embodiments, the k-th objective dual variable solution includes the k-th spectral dual variable solution, and the (k+1)-th objective auxiliary variable solution includes the (k+1)-th spectral auxiliary variable solution. The (k+1)-th spectral auxiliary variable solution is calculated in the following manner: Based on the (k+1)th spectral data solution, the spectral difference operator matrix, and the kth spectral dual variable solution, the spectral vector after spectral correction is determined. Substituting the spectral vector after spectral correction into the solution equation of the scalar soft threshold operator, we obtain the solution of the spectral auxiliary variable for the (k+1)th iteration.

[0093] The formula for calculating the solution of the (k+1)th spectral auxiliary variable is as follows.

[0094] (15); in, For the (k+1)th spectral auxiliary variable solution, For the solution of the spectral auxiliary variable that needs to be solved, For penalty parameters, The spectral difference operator matrix is... For the k-th spectral dual variable solution, In order to seek Norm, For the (k+1)th spectral data solution, For the total variation weights in the spectral dimension, The solution equations for the scalar soft thresholding operator are as follows: This is the spectral vector after spectral correction.

[0095] This application can accurately calculate the solution of the spectral auxiliary variable for the (k+1)th time by substituting the spectral vector after spectral correction into the solution equation of the scalar soft threshold operator.

[0096] Based on the above embodiments, the residuals of the second optimization equation include the original residuals and the dual residuals, which are determined in the following manner: The original residuals are determined based on the (k+1)th objective auxiliary variable solution and the (k+1)th spectral data solution. The dual residual is determined based on the k-th objective auxiliary variable solution and the (k+1)-th objective auxiliary variable solution.

[0097] The original residual is determined based on the (k+1)th objective auxiliary variable solution and the (k+1)th spectral data solution. Optionally, the original residual is calculated based on the (k+1)th horizontal auxiliary variable solution, the horizontal spatial difference operator matrix, the (k+1)th vertical auxiliary variable solution, the vertical spatial difference operator matrix, the (k+1)th spectral auxiliary variable solution, the spectral difference operator matrix, and the (k+1)th spectral data solution.

[0098] The dual residuals are determined based on the k-th objective auxiliary variable solution and the (k+1)-th objective auxiliary variable solution. Optionally, the dual residuals are calculated based on the k-th horizontal auxiliary variable solution, the (k+1)-th horizontal auxiliary variable solution, the horizontal spatial difference operator matrix, the k-th vertical auxiliary variable solution, the (k+1)-th vertical auxiliary variable solution, the vertical spatial difference operator matrix, the k-th spectral auxiliary variable solution, the (k+1)-th spectral auxiliary variable solution, and the spectral difference operator matrix.

[0099] Setting residuals includes setting the original residual and setting the dual residual. A residual less than the set residual includes the original residual being less than the set original residual, and the dual residual being less than the set dual residual.

[0100] The formulas for calculating the original residual and the dual residual are as follows.

[0101] (16); in, For the original residual, For dual residuals, To set the original residual, To define the dual residual, The horizontal space difference operator matrix, The matrix represents the vertical space difference operator. The spectral difference operator matrix is... For the (k+1)th level auxiliary variable solution, For the (k+1)th spectral data solution, For the (k+1)th vertical auxiliary variable solution, For the (k+1)th spectral auxiliary variable solution, For penalty parameters, For the solution of the auxiliary variable at the k-th level, For the k-th vertical auxiliary variable solution, This is the solution for the auxiliary variable of the k-th spectrum.

[0102] This application can accurately terminate the iterative solution process by calculating the original residual and the dual residual.

[0103] like Figure 2 As shown, the objective auxiliary variable and objective dual variable are iteratively solved in the second optimization equation to iteratively solve the spectral data, specifically including the following steps.

[0104] (1) Set initial spectral data. Determine the initial auxiliary variables and initial dual variables based on the initial spectral data.

[0105] (2) In the second optimization equation, the second spectral data solution is calculated based on the initial auxiliary variables and the initial dual variables. .

[0106] (3) Calculate the second target auxiliary variable solution based on the second spectral data solution and the initial dual variable solution.

[0107] (4) Based on the second objective auxiliary variable solution and the second spectral data solution Given the initial dual variable, calculate the solution for the second objective dual variable.

[0108] (5) Calculate the original residual and dual residual of the second optimization equation. Determine whether the original residual is less than the set original residual and whether the dual residual is less than the set dual residual.

[0109] If the original residual is less than the set original residual, and the dual residual is less than the set dual residual, the iteration termination condition is met. The solution based on the second spectral data is used as the final spectral data.

[0110] If the original residual is greater than or equal to the set original residual, and the dual residual is greater than or equal to the set dual residual, it is determined that the iteration termination condition has not been met. Continue iterating to solve the problem.

[0111] During the iterative solution process, the third spectral data solution is calculated based on the second auxiliary variable solution and the second dual variable solution. Based on the third spectral data solution and the second dual variable solution, calculate the third objective auxiliary variable solution. Calculate the third objective dual variable solution using the second benchmark dual variable solution. Calculate the original residual and the dual residual. Determine whether the original residual is less than the set original residual and whether the dual residual is less than the set dual residual.

[0112] According to the above method, the solution of the spectral data for the (k+1)th iteration is obtained until the residual of the second optimization equation is less than the set residual. The iteration ends and the spectral data solution obtained in the last iteration is taken as the spectral data.

[0113] This application focuses on the color artifacts caused by misalignment of edges between bands in snapshot spectral reconstruction. It proposes a snapshot spectral imaging reconstruction method based on group sparse total variation. By introducing a cross-band mixing norm for solution, it forces all spectral bands to share a consistent spatial edge structure, thereby fundamentally suppressing edge color artifacts caused by inconsistencies in the edges of different bands. From the perspective of edge consistency prior, this application proposes to use a group sparse total variation regularization term (GSTV) to establish the first optimization equation for cross-band edge alignment. Based on the alternating direction multiplier method algorithm framework, this application presents the iterative solution process of the snapshot spectral imaging reconstruction method based on group sparse total variation.

[0114] By comparing the reconstructed spectral data, in spatially uniform and smooth regions, the group sparsity constraint of this application effectively eliminates stripe structure artifacts related to the periodic arrangement of the filter array through cross-band coupling optimization. In the reconstruction of fine structures and small targets, the group sparsity characteristic of the norm in this application suppresses edge color artifact blurring, achieving an effective balance between edge enhancement and noise suppression. Compared to independent total variation methods, it has superior spatial resolution. Furthermore, compared to traditional total variation-based algorithms, this application relies on fewer filters.

[0115] Experimental results show that the spectral data calculated in this application significantly improves objective quality indicators such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and spectral angle mapper (SAM). Furthermore, the snapshot spectral imaging method based on group sparse total variation in this application also exhibits higher spectral fidelity, more accurate edge localization capabilities, and stronger noise robustness.

[0116] The snapshot spectral imaging reconstruction apparatus provided in this application is described below. The snapshot spectral imaging reconstruction apparatus described below can be referred to in correspondence with the snapshot spectral imaging reconstruction method described above.

[0117] A snapshot spectral imaging reconstruction device, comprising: The optimization equation construction module 301 is used to construct a first optimization equation based on the acquired two-dimensional image data from snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixing norm; The optimization equation construction module 302 is used to introduce the target auxiliary variable and the target dual variable of the spectral data into the first optimization equation to obtain the second optimization equation; The solver module 303 is used to solve for the target auxiliary variable and the target dual variable in the second optimization equation in order to solve for the spectral data.

[0118] The snapshot spectral imaging reconstruction apparatus provided in this application constructs a first optimization equation based on the acquired two-dimensional image data of the snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a term based on… Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixture norm is used; a target auxiliary variable and a target dual variable of spectral data are introduced into the first optimization equation to obtain the second optimization equation; the target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data. This application introduces a method based on... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixture norm can enhance the smoothness and coherence of the spatial structure of the reconstructed spectral data, as well as the smoothness of the spectral data in the spectral dimension. It can effectively avoid the differences in spectral data at the edge of the target scene, further avoid color artifacts, and improve the quality of the reconstructed spectral data.

[0119] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0120] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a snapshot spectral imaging reconstruction method. This method includes: constructing a first optimization equation based on the acquired two-dimensional image data from the snapshot spectral imaging, the transmittance tensor of the multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixing norm; the target auxiliary variable and the target dual variable of the spectral data are introduced into the first optimization equation to obtain the second optimization equation; the target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data.

[0121] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0122] Furthermore, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, this computer program is implemented to perform the snapshot spectral imaging reconstruction method provided by the methods described above. This method includes: constructing a first optimization equation based on acquired two-dimensional image data from a snapshot spectral imaging, the transmittance tensor of a multilayer thin-film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a term based on... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixing norm; the target auxiliary variable and the target dual variable of the spectral data are introduced into the first optimization equation to obtain the second optimization equation; the target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A snapshot hyperspectral imaging reconstruction method, characterized in that, include: Based on the collected snapshot hyperspectral imaging two-dimensional image data, the transmittance tensor of the multi-layer film array, and the spectral data to be reconstructed, a first optimization equation is constructed, which includes a data fidelity term, a group sparse total variation term based on the spatial dimension of the mixed norm, and a total variation term based on the spectral dimension of the mixed norm. Based on the collected snapshot hyperspectral imaging two-dimensional image data, the transmittance tensor of the multi-layer film array, and the spectral data to be reconstructed, a first optimization equation is constructed, which includes a data fidelity term, a group sparse total variation term based on the spatial dimension of the mixed norm, and a total variation term based on the spectral dimension of the mixed norm. Based on the collected snapshot hyperspectral imaging two-dimensional image data, the transmittance tensor of the multi-layer film array, and the spectral data to be reconstructed, a first optimization equation is constructed, which includes a data By introducing the target auxiliary variable and the target dual variable of spectral data into the first optimization equation, a second optimization equation is obtained. The target auxiliary variable and the target dual variable are solved in the second optimization equation to solve for the spectral data.

2. The snapshot spectral imaging reconstruction method according to claim 1, characterized in that, Solving for the target auxiliary variable and the target dual variable in the second optimization equation to solve for the spectral data includes: In the second optimization equation, the (k+1)th spectral data solution is calculated based on the kth objective auxiliary variable solution and the kth objective dual variable solution; Based on the (k+1)th spectral data solution and the kth objective dual variable solution, calculate the (k+1)th objective auxiliary variable solution; Based on the (k+1)th objective auxiliary variable solution, the (k+1)th spectral data solution, and the (k)th objective dual variable solution, calculate the (k+1)th objective dual variable solution; Update k+1 to k, and iteratively solve the (k+1)th spectral data solution based on the updated kth objective auxiliary variable solution and the updated kth objective dual variable solution until the residual of the second optimization equation is less than the set residual; the first objective auxiliary variable solution is the initial auxiliary variable; the first objective dual variable solution is the initial dual variable, k≥1; The spectral data solution obtained from the last calculation is used as the spectral data.

3. The snapshot spectral imaging reconstruction method according to claim 1, characterized in that, The target auxiliary variables include horizontal auxiliary variables, vertical auxiliary variables, and spectral auxiliary variables, and the target auxiliary variables are determined based on the following method: The horizontal auxiliary variable is determined based on the horizontal spatial difference operator matrix and the spectral data; The vertical auxiliary variable is determined based on the vertical spatial difference operator matrix and the spectral data; The spectral auxiliary variables are determined based on the spectral difference operator matrix and the spectral data.

4. The snapshot spectral imaging reconstruction method according to claim 2, characterized in that, The k-th objective dual variable solution includes the k-th level dual variable solution, and the (k+1)-th objective auxiliary variable solution includes the (k+1)-th level auxiliary variable solution. The (k+1)-th level auxiliary variable solution is calculated based on the following method: Based on the (k+1)th spectral data solution, the horizontal spatial difference operator matrix, and the kth horizontal dual variable solution, the horizontally corrected spectral vector is determined. Substitute the horizontally corrected spectral vector into Solving the equations for the proximal operators of the norm yields the solution for the (k+1)th level auxiliary variable.

5. The snapshot spectral imaging reconstruction method according to claim 2, characterized in that, The k-th objective dual variable solution includes the k-th vertical dual variable solution, and the (k+1)-th objective auxiliary variable solution includes the (k+1)-th vertical auxiliary variable solution. The (k+1)-th vertical auxiliary variable solution is calculated based on the following method: Based on the (k+1)th spectral data solution, the vertical spatial difference operator matrix, and the kth vertical dual variable solution, the vertically corrected spectral vector is determined. Substitute the vertically corrected spectral vector into Solving the equations for the proximal operators of the norm yields the (k+1)th vertical auxiliary variable solution.

6. The snapshot spectral imaging reconstruction method according to claim 2, characterized in that, The k-th objective dual variable solution includes the k-th spectral dual variable solution, and the (k+1)-th objective auxiliary variable solution includes the (k+1)-th spectral auxiliary variable solution. The (k+1)-th spectral auxiliary variable solution is calculated based on the following method: Based on the (k+1)th spectral data solution, the spectral difference operator matrix, and the kth spectral dual variable solution, the spectral vector after spectral correction is determined; Substituting the spectral vector after spectral correction into the solution equation of the scalar soft threshold operator, the solution of the (k+1)th spectral auxiliary variable is obtained.

7. The snapshot spectral imaging reconstruction method according to claim 2, characterized in that, The residuals of the second optimization equation include the original residuals and the dual residuals, which are determined based on the following: Based on the (k+1)th objective auxiliary variable solution and the (k+1)th spectral data solution, the original residual is determined; The dual residual is determined based on the k-th objective auxiliary variable solution and the (k+1)-th objective auxiliary variable solution.

8. A snapshot spectral imaging reconstruction device, characterized in that, include: An optimization equation construction module is used to construct a first optimization equation based on the acquired two-dimensional image data from snapshot spectral imaging, the transmittance tensor of a multilayer thin film array, and the spectral data to be reconstructed. The first optimization equation includes a data fidelity term and a... Group sparse total variational terms of spatial dimension of mixture norm and based on The total variational term of the spectral dimension of the mixing norm; The optimization equation construction module is used to introduce target auxiliary variables and target dual variables of spectral data into the first optimization equation to obtain the second optimization equation; The solution module is used to solve for the target auxiliary variable and the target dual variable in the second optimization equation to solve for the spectral data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the snapshot spectral imaging reconstruction method as described in any one of claims 1 to 7, characterized in that the method of solving the target auxiliary variable in the second optimization equation is described.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the snapshot spectral imaging reconstruction method as described in any one of claims 1 to 7, characterized in that the method of solving the target auxiliary variable in the second optimization equation is described.

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