Hyperspectral image (HSI) super-resolution, which reconstructs a high-resolution HSI (HR-HSI) through hyperspectral and multispectral image fusion (HMIF) tasks that integrate a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI), has emerged as a promising technique for enhancing spatial–spectral quality. Recently, low-rank representations have demonstrated significant advances in various hyperspectral-related applications, providing an effective solution to HMIF tasks. However, most existing methods rely on model priors to learn the low-rank representation of HSIs, which restricts their adaptability to low-rank variations across different datasets. To address this issue, this paper introduces a self-supervised low-rank decomposition network (SSLRDN) framework specifically designed for HMIF, inspired by the observation that the HR-MSI and HR-HSI of the same scene share highly similar spatial features, whereas different hyperspectral scenes exhibit variations in both spectral and spatial features. In SSLRDN, we develop a self-supervised network to adaptively learn the low-rank decomposition (spectral subspace and spatial coefficients) across different HR-HSIs, overcoming the inefficiency of conventional alternating optimization methods where factor updates fail to mutually promote each other. Given the spatial feature consistency between HR-MSI and HR-HSI, we leverage the rich spatial information from HR-MSI to guide the learning of spatial coefficients in HR-HSI. To enhance the self-supervised learning of spatial coefficient images, we further integrate an externally pre-trained denoiser to improve their estimation accuracy, effectively fusing and mutually promoting both self-supervised and pre-trained learning paradigms. Experimental results show that the proposed method achieves superior performance in both visual quality and quantitative metrics, without requiring pretraining on external datasets.

Hyperspectral images (HSIs) often suffer from diverse and unknown degradations during imaging, leading to severe spectral and spatial distortions. Existing HSI restoration methods typically rely on specific degradation assumptions, limiting their effectiveness in complex scenarios. In this paper, we propose \textbf{MP-HSIR}, a novel multi-prompt framework that effectively integrates spectral, textual, and visual prompts to achieve universal HSI restoration across diverse degradation types and intensities. Specifically, we develop a prompt-guided spatial-spectral transformer, which incorporates spatial self-attention and a prompt-guided dual-branch spectral self-attention. Since degradations affect spectral features differently, we introduce spectral prompts in the local spectral branch to provide universal low-rank spectral patterns as prior knowledge for enhancing spectral reconstruction. Furthermore, the text-visual synergistic prompt fuses high-level semantic representations with fine-grained visual features to encode degradation information, thereby guiding the restoration process. Extensive experiments on 9 HSI restoration tasks, including all-in-one scenarios, generalization tests, and real-world cases, demonstrate that MP-HSIR not only consistently outperforms existing all-in-one methods but also surpasses state-of-the-art task-specific approaches across multiple tasks. The code and models are available at https://github.com/ZhehuiWu/MP-HSIR.

Snapshot compressive imaging (SCI) captures a 3D hyperspectral image (HSI) using a 2D compressive measurement and reconstructs the desired 3D HSI from that 2D measurement. The effective reconstruction method thus is crucial in SCI. Despite recent successes of deep learning (DL)-based methods over traditional approaches, they often ignore the intrinsic characteristics of HSI and are trained for a specific imaging system using sufficient paired datasets. To address this, we propose a novel self-supervised HSI reconstruction framework called low-rank tensor meets deep prior (LDMeet), which couples model-driven and data-driven methods. The design of LDMeet is inspired by the traditional model-driven low-rank tensor prior constructed based on domain knowledge, which can explore the intrinsic global spatial-spectral correlation of HSI and make the reconstruction method interpretable. To further utilize the powerful learning ability of DL-based approaches, we introduce a self-supervised spatial-spectral guided network (SSG-Net) into LDMeet to learn the implicit deep spatial-spectral prior of HSI without requiring training data, making it adaptable to various imaging systems. An efficient alternating direction method of multiplier (ADMM) is designed to solve the LDMeet model. Comprehensive experiments confirm that our LDMeet achieves superior results compared to self-supervised HSI reconstruction methods, while also yielding competitive results with supervised learning methods.

Optical remote sensing images are inevitably affected by thick cloud cover, leading to information missing, which seriously hinders subsequent Earth observation tasks. Existing cloud removal methods typically focus on extracting either global or local features, but lack effective fusion of these two aspects, resulting in deficiencies in recovering global structure and fine details. However, fusing multiple features is challenging for single-driven methods due to the diversity of images and clouds. To this end, this paper proposes a novel joint-driven cloud removal framework that can simultaneously exploit the global structural and local deep features of both the image and cloud components. Specifically, in our joint-driven scheme, we devise a flexible model-driven low-rank group sparse decomposition method, in which the global temporal-spectral correlations and shared sparse features of multitemporal remote sensing images and clouds from different scenes are effectively captured. To fully account for the differences in the local features of images and clouds from different scenes, we devise effective data-driven dual self-supervised networks to adaptively learn their local deep features. Additionally, we design an efficient half-quadratic splitting algorithm to iteratively optimize the joint-driven model, fostering mutual enhancement between the two and achieving further performance improvements. Experimental results show that the proposed method outperforms existing thick cloud removal approaches in both simulated and real-world datasets, under varying factors such as spectral bands, cloud coverage, time nodes, and spatial resolutions.

Thick cloud removal for multitemporal remote sensing images (MTRSIs) is a necessary preprocessing step for subsequent applications. Existing methods for cloud removal ignore spectral–temporal consistency prior (STCP), such as smooth regions of different time and bands existing in the same spatial location. To address this problem, we propose a factor-based group sparsity regularization within the low-rank tensor factorization (LRTF) framework and theoretically prove that it can characterize the STCP in MTRSIs. Based on this regularization, we construct a cloud removal model for MTRSIs. On one hand, the introduction of STCP enables the model to achieve superior cloud removal performance. On the other hand, regularization on small-sized factors rather than on the original data enables the model to have extremely low computational complexity. To solve this model, we develop a proximal alternating minimization (PAM)-based algorithm, in which we integrate a mask acquisition method based on separated cloud and shadow components. Comparative experiments using both simulated and real data demonstrate that the proposed method outperforms recent mask-unknown and mask-known methods in terms of performance and efficiency.

Due to imaging hardware limitations and atmospheric interference, hyperspectral image (HSI) often suffers from low spatial resolution or mixed noise degradation. HSI fusion and denoising are two key strategies to improve HSI quality. Traditional model-based methods rely on data-specific manual priors, while supervised deep learning methods are typically developed specifically for a single task and require a large number of training datasets. To address these limitations, we propose a novel unsupervised feature fusion-guided network (UFFGNet) as a general prior that effectively leverages multi-scale semantic features from a guidance image while incorporating sparse prior constraints to suppress outliers. Specifically, UFFGNet comprises a deep feature extraction network to capture multiscale semantic features from a guidance image, and an attentionbased feature generation network that generates an output image from random noise. These two networks are connected by a feature refinement module to embed the refined features from the feature aggregation module into the generation network. Furthermore, the sparse prior constraint is incorporated to model sparse noise (including impulse noise, stripe artifacts, and deadlines) in HSI, thereby improving the robustness of UFFGNet. The proposed network optimizes network parameters in an unsupervised manner without external additional training data, using the fidelity term in the degradation model as a loss function to learn the prior information of the original image. Extensive experiments demonstrate that the proposed UFFGNet outperforms state-of-the-art methods in both HSI fusion and denoising tasks, significantly improving HSI quality.

Recently, subspace representation-based methods have shown notable success in hyperspectral image (HSI) denoising by exploiting the spectral correlation of HSIs. However, these methods cannot fully explore the spatial correlation within HSIs, resulting in unsatisfactory results. To address this limitation, we propose a novel generalized Kronecker decomposition-based subspace representation (GKDSR) method, which can simultaneously characterize the spatial and spectral correlation of HSIs. Specifically, we first decompose an HSI as the spectral subspace and coefficients. Then we impose the face-wise generalized Kronecker decomposition to the coefficients to fully characterize the spatial correlation of the HSI. Armed with the proposed GKDSR method, we build a GKDSR-based HSI denoising model, which can not only achieve a promising spectral fidelity but also preserve spatial fine details. To solve the proposed HSI denoising model, we develop an efficient alternating minimization-based algorithm. Experimental results demonstrate the significant superiority of the proposed method compared with competing methods in terms of spectral fidelity and preservation of spatial details.

In this paper, we propose an ℓ0-based nonconvex optimization model with overlapping group sparse hyper-Laplacian prior (ℓ0-OGSHL) to remove stripes from remote sensing images (RSIs) effectively. Specifically, we utilize the hyper-Laplacian prior with overlapping group sparsity (OGSHL) to characterize the properties of the underlying image. Additionally, the related ℓ0-quasi equivalent is transformed into an easily solvable form by employing a mathematical program with equilibrium constraints (MPEC). Furthermore, the alternating direction method of multipliers (ADMM) algorithm is employed for resolving the equivalent nonconvex optimization model, and the complex OGSHL subproblem is addressed through the majorization-minimization (MM) method. Finally, the experimental results on the simulated datasets conclusively demonstrate the superior performance of the proposed method over the compared methods (with 1 3dB higher MPSNR), both quantitatively and visually. The code will be available after possible acceptance.

In this letter, we propose a novel unidirectional spatial and spectral smoothed tensor ring (U3STR) decomposition for hyperspectral image (HSI) denoising and destriping. The powerful tensor ring (TR) decomposition is introduced to explore the global spatial-spectral correlation of HSI, which transforms the restoration of HSI into estimating three TR factors. To address the local spatial-spectral smoothness of HSI and the directional characteristic of stripe noise, unidirectional spatial and spectral smoothed constraints are applied to the horizontal spatial and spectral TR factors, respectively. Moreover, considering that the stripe noise shares spatial correlation and local smoothness with the image component, we strategically utilize band-by-band low rank and unidirectional total variation (TV) regularization, effectively disentangling stripe noise from the image content without conflicting the image regularization. The proposed U3STR model is solved by the alternating direction method of multipliers (ADMM) algorithm effectively. Experimental results demonstrate that our method outperforms other HSI restoration methods in denoising and destriping, notably enhancing the quality of the restored image by an average of 3 dB over existing methods.

The existence of thick clouds covers the comprehensive Earth observation of optical remote sensing images (RSIs). Cloud removal is an effective and economical preprocessing step to improve the subsequent applications of RSIs. Deep learning (DL)-based methods have attracted much attention and achieved state-of-the-art results. However, most of these methods suffer from the following issues: 1) ignore the physical characteristics of RSIs; 2) require paired images with/without cloud or extra auxiliary images (such as SAR); and 3) demand the cloud mask. These issues might have limited the flexibility of existing networks. In this paper, we propose a novel low-rank regularized self-supervised network (LRRSSN) that couples model-driven and data-driven methods to remove the thick cloud from multitemporal remote sensing images (MRSIs). First, motivated by the equal importance of image and cloud components as well as their intrinsic characteristics, we decompose the observed image into low-rank image and structural sparse cloud components. In this way, we obtain a model-driven thick cloud removal method where the spectral-temporal low-rank correlation of the image component and the spectral structural sparsity of the cloud component are effectively exploited. Second, to capture the complex nonlinear features of different scenarios, the data-driven self-supervised network that does not require external training datasets is designed to explore the deep prior of the image component. Third, the coupled model-driven and data-driven LRRSSN is optimized by an efficient half-quadratic splitting algorithm. Finally, without knowing the exact cloud mask, we estimate the cloud mask to preserve information in cloud-free areas as much as possible. Experiments conducted in synthetic and real-world scenarios demonstrate the effectiveness of the proposed approach.

Denoising of hyperspectral image (HSI) is challenging, especially when dealing with large-scale data. Model-based methods show promise in HSI denoising due to their good generalization, but they suffer from computational complexity due to complex priors [like nonlocal self-similarity (NSS)] and iterations, resulting in low efficiency for large-scale HSI processing. To address these challenges, we propose a fast large-scale HSI denoising (FallHyDe) method based on noniterative low-rank (LR) subspace representation to enjoy high denoising efficiency, effectiveness, and flexibility simultaneously. By leveraging the global spectral property of HSI, FallHyDe efficiently estimates spectral subspace and spatial representation coefficients (SRCs) from the observed noisy HSI, reducing computation complexity caused by the high spectral dimension during processing. In addition, we innovatively explore the presence of high signal-to-noise ratio bands (HSNRBs) in real HSI, enabling fast SRC estimation through a least squares problem without relying on complex priors and iterations. FallHyDe requires neither iteration nor parameter tuning, enabling our method to process large-scale HSI denoising quickly and flexibly. Experimental results on both simulated and real HSI datasets demonstrate that our proposed method not only achieves competitive results in quality but also speeds up the restoration by more than ten times than the representative fast HSI denoising methods. The code is available athttps://chenyong1993.github.io/yongchen.github.io/.

Haze pollution in hyperspectral images (HSIs) leads to surface information lack and image clarity degradation, which seriously affects the performance of subsequent image interpretation. Existing model-based hyperspectral haze removal methods enjoy good interpretability and generalization, but they can only process images in a specific wavelength range due to the principle limitation. Deep learning-based dehazing methods have good feature extraction capability, but the cost of acquiring sufficient training data is high in practical applications. At the same time, taking into account that HSIs have spectral low-rank structures, fully utilizing the low-rank property will facilitate the reconstruction of HSIs. In order to combine the complementary benefits of deep learning-based and physical model-based approaches, we decide to formulate HSI dehazing reconstruction as an unsupervised DIP framework. Specifically, we propose an unsupervised dehazing network with hybrid prior constraints (HPC-UDN) for HSI haze removal, which effectively integrates low-rank prior, deep priors, and physical haze prior. First, the low-rank prior of hyperspectral data is characterized by matrix decomposition, where the decomposition factors are learned through two generative networks. Then, multiple spectral groups are divided based on the correlation and complementarity between spectral bands. In order to exchange information between adjacent spectral groups, a novel spectral grouping feature fusion module is designed, which connects neighboring spectral groups to transfer spectral and spatial features. Finally, high-quality HSI is recovered by merging the features extracted from each spectral group. Extensive simulated and real-data experiments certify the effectiveness and robustness of the presented unsupervised approach and potential applications in the GF-5 image dehazing task.

Coded aperture snapshot spectral imaging (CASSI) is an important technique for capturing three-dimensional (3D) hyperspectral images (HSIs), and involves an inverse problem of reconstructing the 3D HSI from its corresponding coded 2D measurements. Existing model-based and learning-based methods either could not explore the implicit feature of different HSIs or require a large amount of paired data for training, resulting in low reconstruction accuracy or poor generalization performance as well as interpretability. To remedy these deficiencies, this paper proposes a novel HSI reconstruction method, which exploits the global spectral correlation from the HSI itself through a formulation of model-driven low-rank subspace representation and learns the deep prior by a data-driven self-supervised deep learning scheme. Specifically, we firstly develop a model-driven low-rank subspace representation to decompose the HSI as the product of an orthogonal basis and a spatial representation coefficient, then propose a data-driven deep guided spatial-attention network (called DGSAN) to adaptively reconstruct the implicit spatial feature of HSI by learning the deep coefficient prior (DCP), and finally embed these implicit priors into an iterative optimization framework through a self-supervised training way without requiring any training data. Thus, the proposed method shall enhance the reconstruction accuracy, generalization ability, and interpretability. Extensive experiments on several datasets and imaging systems validate the superiority of our method. The source code and data of this article will be made publicly available at https://github.com/ChenYong1993/LRSDN.

Snapshot compressive imaging (SCI) is a promising technique that captures a 3-D hyperspectral image (HSI) by a 2-D detector in a compressed manner. The ill-posed inverse process of reconstructing the HSI from their corresponding 2-D measurements is challenging. However, current approaches either neglect the underlying characteristics, such as high spectral correlation, or demand abundant training datasets, resulting in an inadequate balance among performance, generalizability, and interpretability. To address these challenges, in this article, we propose a novel approach called LR2DP that integrates the model-driven low-rank prior and data-driven deep priors for SCI reconstruction. This approach not only captures the spectral correlation and deep spatial features of HSI but also takes advantage of both model-based and learning-based methods without requiring any extra training datasets. Specifically, to preserve the strong spectral correlation of the HSI effectively, we propose that the HSI lies in a low-rank subspace, thereby transforming the problem of reconstructing the HSI into estimating the spectral basis and spatial representation coefficient. Inspired by the mutual promotion of unsupervised deep image prior (DIP) and trained deep denoising prior (DDP), we integrate the unsupervised network and pre-trained deep denoiser into the plug-and-play (PnP) regime to estimate the representation coefficient together, aiming to explore the internal target image prior (learned by DIP) and the external training image prior (depicted by pre-trained DDP) of the HSI. An effective half-quadratic splitting (HQS) technique is employed to optimize the proposed HSI reconstruction model. Extensive experiments on both simulated and real datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Dataflow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically typed programming languages like Python present implicit dataflow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit dataflow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit dataflow problems. To further enhance the accuracy of LLMs, we design a five-step chain of thought (CoT) and break it down into an Artificial Intelligence (AI) chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit dataflow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.

Hyperspectral image (HSI) mixed noise removal is a fundamental problem and an important preprocessing step in remote sensing fields. The low-rank approximation-based methods have been verified effective to encode the global spectral correlation for HSI denoising. However, due to the large scale and complexity of real HSI, previous low-rank HSI denoising techniques encounter several problems, including coarse rank approximation (such as nuclear norm), the high computational cost of singular value decomposition (SVD) (such as Schatten$p$-norm), and adaptive rank selection (such as low-rank factorization). In this article, two novel factor group sparsity-regularized nonconvex low-rank approximation (FGSLR) methods are introduced for HSI denoising, which can simultaneously overcome the mentioned issues of previous works. The FGSLR methods capture the spectral correlation via low-rank factorization, meanwhile utilizing factor group sparsity regularization to further enhance the low-rank property. It is SVD-free and robust to rank selection. Moreover, FGSLR is equivalent to Schatten$p$-norm approximation (Theorem 1), and thus FGSLR is tighter than the nuclear norm in terms of rank approximation. To preserve the spatial information of HSI in the denoising process, the total variation regularization is also incorporated into the proposed FGSLR models. Specifically, the proximal alternating minimization is designed to solve the proposed FGSLR models. Experimental results have demonstrated that the proposed FGSLR methods significantly outperform existing low-rank approximation-based HSI denoising methods.

Hyperspectral image (HSI) denoising has been regarded as an effective and economical preprocessing step in data subsequent applications. Recent nonlocal low-rank approximation on each full band patch group has demonstrated their superiority for HSI denoising. These methods, however, directly design the low-rank regularization to the grouped patch image itself (i.e., original domain), which ignores the spatial information of the grouped patch image and cannot explores the potential structure. To address these issues, this paper proposes a nonlocal group sparsifying transform learning method (dubbed TLNLGS) for HSI denoising. Motivated by the global spectral correlation in the HSI, we firstly impose a certain low-dimensional subspace hypothesis over the HSI to prevent the heavy computation burden with the spectral band increases, and then explore a discriminatively intrinsic nonlocal group sparse prior of the reduced image by transform model. The learned group sparse prior can not only excavate the nonlocal self-similarity as recent nonlocal low-rank approximation methods but also preserve the local spatial smooth structure of the image. Moreover, compared with the fixed transform domain (e.g., gradient and discrete cosine transformation domains), the transform learning scheme can improve the sparse representation ability. An efficient block coordinate descent (BCD) algorithm is developed to solve the proposed model. Extensive experiments, including simulated and real HSI datasets, indicate the superiority of the proposed TLNLGS method over the state-of-the-art HSI denoising approaches.

Fusing a pair of low-spatial-resolution hyperspectral image (LR-HSI) and high-spatial-resolution multispectral image (HR-MSI) has been regarded as an effective and economical strategy to achieve HR-HSI, which is essential to many applications. Among existing fusion models, the tensor ring (TR) decomposition-based model has attracted rising attention due to its superiority in approximating high-dimensional data compared to other traditional matrix/tensor decomposition models. Unlike directly estimating HR-HSI in traditional models, the TR fusion model translates the fusion procedure into an estimate of the TR factor of HR-HSI, which can efficiently capture the spatial–spectral correlation of HR-HSI. Although the spatial–spectral correlation has been preserved well by TR decomposition, the spatial–spectral continuity of HR-HSI is ignored in existing TR decomposition models, sometimes resulting in poor quality of reconstructed images. In this article, we introduce a factor smoothed regularization for TR decomposition to capture the spatial–spectral continuity of HR-HSI. As a result, our proposed model is calledfactor smoothed TR decompositionmodel, dubbedFSTRD. In order to solve the suggested model, we develop an efficient proximal alternating minimization algorithm. A series of experiments on four synthetic datasets and one real-world dataset show that the quality of reconstructed images can be significantly improved by the introduced factor smoothed regularization, and thus, the suggested method yields the best performance by comparing it to state-of-the-art methods.

The existing nonblind cloud and cloud shadow (cloud/shadow) removal methods for remote sensing (RS) images are based on the assumption that cloud/shadow masks are accurately given. Since the masks are usually manually labeled or detected by cloud detection methods, whose accuracy cannot be well guaranteed, the cloud/shadow removal effect may be affected. In this article, we suggest a robust thick cloud/shadow removal (RTCR) method that meets the problem with an inaccurate mask. To faithfully reconstruct the multitemporal information, a coupled tensor factorization is used to explore the relationship between the abundances of the multitemporal images in the same scene. Moreover, an efficient algorithm is developed to solve the proposed model based on the augmented Lagrange multiplier method. The experimental results under accurate masks and inaccurate masks demonstrate its robustness and superiority for thick cloud/shadow removal.

For multi-temporal remote sensing (RS) images, the distribution of surface materials is constant concerning time and the same material shows different spectral features at different times. Decomposing the image at each time into an abundance tensor and temporal features, there is a strong similarity between abundance tensors of all time. Based on this observation, we suggest a blind thick cloud/shadow removal model, which exploits the sparsity of the cloud component and the similarity between abundance tensors, achieving both cloud detection and multi-temporal information restoration. Moreover, a mask refinement strategy is designed to pursue the optimal cloud/shadow mask. We develop an efficient algorithm to solve the proposed model based on the augmented Lagrange multiplier method. The results of simulated experiments in different scenarios verify the superiority of the proposed method for thick cloud/shadow removal.

The aim of hyperspectral image super-resolution (HSI-SR) is to produce high spatial resolution hyperspectral image (HR - HSI) by exploiting the available high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI). In this work, we develop a novel matrix factorization (MF)-based HSI -SR way, which formulates the HSI -SR problem as estimating the spectral dictionary from the observed LR - HSI and the coefficient matrix from both the observed HR-MSI and LR-HSI. Specifically, we first estimate the spectral dictionary from the observed LR - HSI by the dictionary learning algorithm with redundancy assumption. Moreover, based on the superpixel segmentation technology used in the observed HR-MSI, the coefficient vectors are grouped. By concatenating the joint-sparse, nonlocallow-rank, and nonnegative priors of the grouped coefficient vectors, we develop a novel coefficient matrix estimation variational model, which fully explores the nonlocal self-similarity of the desired HR-HSI. The proposed coefficient matrix estimation model is solved under the alternating direction method of multipliers (ADMM) framework. Experimental results prove the superiority of the proposed way from the quantitative and qualitative analysis.

Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent processing accuracy. By encoding sparse prior to the spatial or spectral difference images, total variation (TV) regularization is an efficient tool for removing the noises. However, the previous TV term cannot maintain the shared group sparsity pattern of the spatial difference images of different spectral bands. To address this issue, this article proposes a group sparsity regularization of the spatial difference images for HSI restoration. Instead of using ℓ1or ℓ2-norm (sparsity) on the difference image itself, we introduce a weighted ℓ2,1norm to constrain the spatial difference image cube, efficiently exploring the shared group sparse pattern. Moreover, we employ the well-known low-rank Tucker decomposition to capture the global spatial-spectral correlation from three HSI dimensions. To summarize, a weighted group sparsity-regularized low-rank tensor decomposition (LRTDGS) method is presented for HSI restoration. An efficient augmented Lagrange multiplier algorithm is employed to solve the LRTDGS model. The superiority of this method for HSI restoration is demonstrated by a series of experimental results from both simulated and real data, as compared with the other state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods.

Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition [Tucker or CANDECOMP/PARAFAC (CP)] methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations.

As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many subsequent applications. Recently, based on the framework of subspace representation and low-rank matrix/tensor factorization (LRMF/LRTF), many single-factor-regularized methods add various regularizations on the spatial factor to characterize its spatial prior knowledge. However, these methods neglect the common characteristics among different bands and the spectral continuity of HSIs. To tackle this issue, this article establishes a bridge between the factor-based regularization and the HSI priors and proposes a double-factor-regularized LRTF model for HSI mixed noise removal. The proposed model employs LRTF to characterize the spectral global low rankness, introduces a weighted group sparsity constraint on the spatial difference images (SpatDIs) of the spatial factor to promote the group sparsity in the SpatDIs of HSIs, and suggests a continuity constraint on the spectral factor to promote the spectral continuity of HSIs. Moreover, we develop a proximal alternating minimization-based algorithm to solve the proposed model. Extensive experiments conducted on the simulated and real HSIs demonstrate that the proposed method has superior performance on mixed noise removal compared with the state-of-the-art methods based on subspace representation, noise modeling, and LRMF/LRTF.

Hyperspectral image compressive sensing reconstruction (HSI-CSR) can largely reduce the high expense and low efficiency of transmitting HSI to ground stations by storing a few compressive measurements, but how to precisely reconstruct the HSI from a few compressive measurements is a challenging issue. It has been proven that considering the global spectral correlation, spatial structure, and nonlocal self-similarity priors of HSI can achieve satisfactory reconstruction performances. However, most of the existing methods cannot simultaneously capture the mentioned priors and directly design the regularization term to the HSI. In this article, we propose a novel subspace-based nonlocal tensor ring decomposition method (SNLTR) for HSI-CSR. Instead of designing the regularization of the low-rank approximation to the HSI, we assume that the HSI lies in a low-dimensional subspace. Moreover, to explore the nonlocal self-similarity and preserve the spatial structure of HSI, we introduce a nonlocal tensor ring decomposition strategy to constrain the related coefficient image, which can decrease the computational cost compared to the methods that directly employ the nonlocal regularization to HSI. Finally, a well-known alternating minimization method is designed to efficiently solve the proposed SNLTR. Extensive experimental results demonstrate that our SNLTR method can significantly outperform existing approaches for HSI-CSR.

Total variation (TV) regularization has been widely used in the hyperspectral image (HSI) mixed noise removal problem by utilizing ℓ1-norm to constrain the spatial difference image and promote the piecewise smooth structure. Unfortunately, it cannot depict the group sparse structure of spatial difference image along the spectral dimension. This paper proposes a new HSI restoration method using weighted group sparsity regularized low-rank tensor decomposition (LRTDGS). Specifically, we use a weighted group sparsity regularization which is denoted by ℓ2,1-norm to explore the group structure of spatial difference image along the spectral dimension. Moreover, the spatial-spectral correlation from three directions of HSI is depicted by low-rank Tucker decomposition. We use efficient augmented Lagrange multiplier method to optimize the proposed LRTDGS model, and a series of experimental results are presented to demonstrate the effectiveness of the proposed method.

This paper proposes a spatial-spectral total variation (TV) regularized low-rank sparsity decomposition model for blind cloud and cloud shadow (cloud/shadow) detection and removal of multitemporal remote sensing imagery. Our concept is to decompose the contaminated image into the surface-reflected component and the cloud/shadow component. Low-rank regularization is utilized to model the spectral-temporal correlation of the surface-reflected component, meanwhile, the `1-norm and spatial-spectral total variation regularization is employed to describe the sparse prior and spatial-spectral continuity of the cloud/shadow component. To better preserve the information in cloud/shadow-free areas, the cloud/shadow detection results obtained as a by-product of our method are used to guide the information compensation from the original contaminated images. Several experiments are presented to demonstrate the effectiveness of the proposed method.

This paper commits to remove the stripe noise to enhance the visual quality of remote sensing images, in the meanwhile preserves image details of stripe-free regions. Instead of solving the underlying image as most of researches, we propose a non-convex l0model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise. Moreover, the proposed non-convex model can be solved by the proximal alternating direction method of multipliers (PADMM) method which theoretically guarantees converging to a KKT point. Extensively experimental results on simulated and real data demonstrate that the proposed method outperforms recent state-of-the-art destriping methods, both visually and quantitatively.