For scenes with complex backgrounds and weak anomalies, how to effectively distinguish anomaly targets from the background is the key to perform hyperspectral image anomaly detection (AD). Data decomposition-based methods have been widely studied due to their potential in separating background and anomaly components. However, due to its unclean background extraction and sensitivity to noise, it has an adverse effect on the detection of anomaly targets. Additionally, a large amount of spectral data can lead to an increase in computation during data decomposition. To address this issue, we propose an AD method based on a feedback band group and variation low-rank sparse model (FBGVLRS-AD). Firstly, we employ a uniform band selection strategy to partition spectral bands and perform data decomposition on the selected band group, to separate low-rank and sparse components. This decomposition on the band group can reduce computational time and mitigate the interference from spectral variability. Secondly, to preserve the integrity of abnormal target spectra during the background extraction process, the L _2,1 norm is employed for joint correlated total variation to extract the desired anomalous targets. Then, utilizing the detection information from the existing band groups, a feedback-driven iterative framework has been designed to consider the consistency and complementarity in AD across band groups. This framework facilitates the extraction of sparse components in subsequent band groups and reinforces the anomalous elements. Iteratively addressing these sub-problems on band groups helps prevent the loss of useful spectral information, maintaining sufficient anomaly information while reducing interference from redundant information and spectral variations. Finally, the proposed FBGVLR-AD is optimally solved by the augmented Lagrange multiplier (ALM) method. Comparison with state-of-the-art anomaly detectors on multiple data validates the competitiveness of the proposed method for AD tasks.

Blurring and noise degrade the performance of image processing. To mitigate this effect, various regularization-based deblurring methods have been proposed. Total variation regularization is widely used owing to its excellent ability in preserving the salient edges, but it also tends to smooth the image details. In this paper, we propose a local extremum-constrained total variation (LECTV) framework for image deblurring. In the developed deblurring framework, we integrate prior knowledge of the dark channel with the structural features of the image into a single regularization term. Furthermore, unlike most existing methods that focus on the overall sparsity of the dark channel, the defined regularization term allows for a pixel-wise adaptive description of the image to restore its inherent spatial texture structure. Finally, a majorization-minimization-based method is designed to solve the developed LECTV framework. Experimental results on natural and hyperspectral images show that the designed framework exhibits excellent performance in removing multiple types and degrees of blurring. Extensive evaluations also further show its superiority compared to other advanced methods.

Hyperspectral images (HSIs) are often disturbed by various kinds of noises. This paper proposes a global variation and local structure low-rank model (GLLR) for HSI denoising by integrating spatial segmentation smoothing and spectral low-rank properties. Compared with existing denoising methods, the proposed method considers not only the global low-rank property but also the local structure low-rank property of HSIs. Specifically, the GLLR describes the global correlation and segmental smoothing structure of the HSI by the correlated total variation. In addition, we construct a new structural low-rank prior, called the local minimum difference (LMD) low-rank. With LMD low-rank property of HSI, GLLR can remove noise while retaining useful structural information in the HSI. Then, an ALM-based optimization algorithm is devised to solve the objective functions for the presented model. Finally, comparison experiments with existing methods are conducted on synthetic and real datasets to demonstrate the effectiveness and superiority of the proposed method.

Hyperspectral anomaly detection (HAD) aims at effectively separating the anomaly target from the background. The low-rank and sparse matrix decomposition (LRaSMD) technique has shown great potential in HAD tasks. However, some LRaSMD models need to convert the hyperspectral data into a two-dimensional matrix. This cannot well maintain the characteristics of the hyperspectral image (his) in each dimension, thus degenerating its representation capacity. In this context, this article proposes a tensor-based Go decomposition (GODEC) model, called TGODEC. The TGODEC model supports the idea of GODEC, representing the HSI data as a combination of background tensor, anomaly tensor, and noise tensor. In detail, the background tensor is solved by the tensor singular value hard thresholding decomposition. The anomaly tensor is solved by a mapping matrix using the corresponding sparse cardinality. Interestingly, the obtained background and anomaly tensors can also be developed for HAD, thus a TGODEC-based anomaly detector is established, called TGODEC-AD. Specifically, the TGODEC-AD method combines the typical RX-AD and R-AD with the above decomposition result of the TGODEC model and constructs different modal operator detectors. Experimental results on multiple real hyperspectral datasets verify the effectiveness of the TGODEC and TGODEC-AD methods. It means that the proposed TGODEC model can effectively characterize the spatial structural features of HSI. As a result, the pure decomposed components can be obtained, contributing to detecting the anomaly target and suppressing the background better in HAD tasks.

Due to high similarity interference of photovoltaic images, repetitive EL image detection and photovoltaic image stitching pose a great challenge. This article proposes two application algorithms and a registration method, which are repetitive EL image detection and photovoltaic image stitching based on the proposed AKAZE-LATCH+GMS registration method. Considering the existence of duplicate EL images acquired indoors, a repetitive EL image detection based on global feature consistency and local cell's texture similarity is proposed. The AKAZE-LATCH+GMS is used to calculate global consistent feature ratio and dHash is used to quantify local cell's similarity to determine whether it is repetitive or not. To solve the problem of high similarity of outdoor photovoltaic images, which leads to dislocation of stitching, an image stitching algorithm based on geographic information is proposed in this article. Compared with AKAZE+GMS and ORB+GMS, the registration experimental results show that the AKAZE-LATCH+GMS method can obtain more initial matching features and higher correct matching rate. Indoor EL images with geometric and illumination differences are used to verify the proposed detection method, which can effectively eliminate repetitive EL images. Aerial photovoltaic images, acquired from the same or different view, are used to verify the validity of the stitching method, and the photovoltaic images can be stitched correctly compared with the stitching method without geographic information.

Anomaly detection is susceptible to complex background and interference noise. Local anomaly detection and collaborative representation detection can effectively suppress background. However, they both encounter the problem of window size determination due to unexpectable target size, which will strongly limit their detection performance. On the other hand, some interferences show similar properties with anomalies, namely small, weak and in low probability, which would be wrongly reputed as anomalies and degrade the detection accuracy too. A global information and structure tensor guided collaborative representation method (GSGCRD) is proposed in this paper. Firstly, the global and local information are combined into the collaborative representation to obtain a more valuable background dictionary, preventing the big targets from being missed. Secondly, to address the problem that anomaly targets are susceptible to interference spectra, an improved structure tensor is introduced into the band selection and detection refining. Bands of better spatial representation for anomaly are selected for detecting, and interference spectra are removed from representation residual to refine the detection result. Experiments on the real datasets show that the method performs better than classical and some recently proposed methods in background and interference suppression, and achieves satisfactory anomaly detection precision.