Event-based motion deblurring aims at reconstructing a sharp image from a single blurry image and its corresponding events triggered during the exposure time. Existing methods learn the spatial distribution of blur from blurred images, then treat events as temporal residuals and learn blurred temporal features from them, and finally restore clear images through spatio-temporal interaction of the two features. However, due to the high coupling of detailed features such as the texture and contour of the scene with blur features, it is difficult to directly learn effective blur spatial distribution from the original blurred image. In this paper, we provide a novel perspective, i.e., employing the blur indication provided by events, to instruct the network in spatially differentiated image reconstruction. Due to the consistency between event spatial distribution and image blur, event spatial indication can learn blur spatial features more simply and directly, and serve as a complement to temporal residual guidance to improve deblurring performance. Based on the above insight, we propose an event-based motion deblurring network consisting of a Multi-Scale Event-based Double Integral (MS-EDI) module designed from temporal residual guidance, and a Blur-Aware Filter Prediction (BAFP) module to conduct filter processing directed by spatial blur indication. The network, after incorporating spatial residual guidance, has significantly enhanced its generalization ability, surpassing the best-performing image-based and event-based methods on both synthetic, semi-synthetic, and real-world datasets. In addition, our method can be extended to blurry image super-resolution and achieves impressive performance. Our code is available at:https://github.com/ChenYichen9527/MBNetnow.

Hyperspectral anomaly detection (HAD) intends to detect potential anomalous targets hidden in the background of hyperspectral images (HSIs) and has garnered substantial attention in various remote sensing photography and surveying applications. Recent research advances in the HAD domain have highlighted the significance of deep convolutional networks (DCNs) and vision transformers (ViTs)-based formulas. However, DCNs are long-range dependency-limited networks, whereas ViTs bear the computational burden of quadratic complexity. Owing to their prominent nonlocal representations and linear complexity, Mamba-based approaches have drawn growing attention. Our study pioneers the integration of Mamba into HAD tasks, presenting CWIMamba, which introduces a novel cross-scale windowed integration state space model for considering the spatial distribution characteristics of the anomaly targets. Specifically, we devise a cross-scale windowed state space model (CSWSSM) to scan the spatial-spectral features based on the window-based bottleneck SSM with different scales. For better multiscale feature integration, a multiscale spatial-spectral feature adaptive integration (MS3FAI) method is explored to generate an intensified representation of multiscale feature interaction and fusion based on the elaborate adaptive spatial-spectral weighting scheme. Moreover, we also devised a Haar discrete wavelet transform convolution module (HDWTCM) to fully replenish the local informative representation and enhance the discriminative frequency characteristics between anomalies and background, introducing more inductive local features for accurate background reconstruction and anomaly suppression. Extensive experiments on five multifarious HAD datasets and seven indicators substantiate the state-of-the-art detection performance, demonstrating the effectiveness of CWIMamba.

Hyperspectral anomaly detection (HAD) is one of the momentous technologies in the field of Earth observation and remote sensing monitoring. Profiting from puissant deep feature extraction abilities, deep convolutional networks (DCN) perform excellently in the HAD domain. Nevertheless, limited by the restriction of unique local receptive fields, DCN-based detection methods struggle to catch the long-range dependence from a global perspective. In contrast, vision transformers (ViTs) perform better in global feature extraction but still disregard the local dependence properties. To this end, we proposed a novel method entitled the global-to-local spatial-spectral awareness transformer (G2LSSAT) network, in which the global transformer block (GTB) and local transformer block (LTB) are deployed in sequence to capture deep reconstruction characteristics from the global view to the local view in a spatial-spectral domain. In particular, the GTB is designed to explore the global spatial-spectral characteristics that are dependent on a crossbar-based global sparse attention module. Furthermore, the global glanced image is divided into multiple local patches and the LTB is devised to learn the local spatial-spectral features supported by a patch-based local self-invisible attention module. In addition, considering that the abnormal pixels always be unexpectedly reconstructed with the conventional self-attention module in ViTs, we introduce a invisible diagonal mask (IDM), which is embedded into the LTB module, to overshadow each pixel itself in the receptive field and reconstruct itself based on global and local dependent spatial-spectral features. Extensive experimental results on six datasets illustrate the superiority of the proposed G2LSSAT compared with other state-of-the-art detectors.

Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology in remote sensing and earth science domains. Nevertheless, most existing detection approaches for anomaly targets flatten 3-D hyperspectral images (HSIs) with spatial and spectral information into 2-D spectral vector data, which virtually breaks up the internal spatial structure in HSIs and degenerates the detection performance. To this end, we directly consider the HSI data cube as a 3-D tensor and develop a novel tensor low-rank approximation (TLRA) detection algorithm to separate the sparse anomalous component from the background with low-rank characteristics. Then, in light of the multi-subspace structure in heterogeneous backgrounds, we utilize multiple subspace learning (MSL) theory to encode the background tensor with a coefficient tensor and corresponding dictionary tensor. In addition, considering that different singular values indicate different information quantities and should be penalized to different extents, we introduce a tighter tensor rank surrogate named the ϵ-shrinkage tensor nuclear norm (ϵ-TNN) to recover the low-rank component more accurately. Meanwhile, concerning the sparse anomaly target, thel2,1constraint is incorporated to represent the group sparsity of the abnormal component. Finally, an effective iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) is devised to solve the proposed TLRA-MSL model. We conduct extensive experiments on six hyperspectral datasets to prove the effectiveness and robustness of our method. The experimental results illustrate that better detection performance is obtained using the proposed model compared with other state-of-the-art algorithms.

In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained once. Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way. Additionally, a plug-and-play model selection module is proposed to search for a spatial-spectral transform domain that is more suitable for AD task than the original data. To establish a unified benchmark to comprehensive evaluate our method and existing methods, we develop a large-scale hyperspectral AD dataset (HAD100) that includes 100 real test scenes with diverse anomaly targets. In comparison experiments, we combine our network with a parameter-free detector, and achieve the optimal balance between detection accuracy and inference speed among state-of-the-art AD methods. Experimental results also show that our method still achieves competitive performance when the training and test set are captured by different sensor devices. Our code is available at https://github.com/ZhaoxuLi123/AETNet.

Infrared dim small target detection is regarded as a critical technology for the interpretation of space-based remote sensing images. In recent years, driven by deep learning technology and the surge of data, remarkable effects have been achieved for dim small target detection in infrared images. Nevertheless, the intrinsic feature scarcity and low signal-to-clutter ratio (SCR) characteristics pose tremendous challenges to deep learning-based detection methods. In this letter, we present a novel sub-pixel sampling cuneate network (SPSCNet) to detect dim small targets in infrared images. The overall model architecture is based on an end-to-end cuneate network with multiple groups of parallel high-to-low resolution subnetworks. Specifically, we design a multi-scale feature reweighted fusion (MSFRF) module to effectively fuse multi-scale feature maps which contain both low-level detail features and high-level semantics information. In addition, considering that the pooling operation may lose dim small targets with low SCR, we also exploit a sub-pixel sampling scheme to greatly retain the features of small targets. Moreover, to better test and verify the performance of the proposed method, we also develop an infrared dim small target (IDST) dataset to conduct more comparative experiments. Extensive experiments on the SIRST and IDST datasets illustrate that the proposed SPSCNet yields state-of-the-art performance in comparison with other detection algorithms.

Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-background modeling. Deep learning has been applied to HAD and achieves promising detection results. However, there exist several issues that need to be addressed: 1) unrealistic Gaussian assumption on the latent representations may limit its application; 2) deep features are not well-suited to anomaly detection due to the separation between feature learning and anomaly detection; 3) lack of adequate exploitation of spectral-spatial features; 4) negative effect caused by spectral band redundancy. In this article, we propose an end-to-end trainable deep one-class classification network for HAD. Specifically, a minimal enclosing hypersphere is trained to involve the deep features of background samples. These background samples are selected by a density clustering-based method. In this way, feature learning and anomaly detection are incorporated into a unified framework. Meanwhile, there is no explicit Gaussian assumption on the background features. Moreover, due to the complementarity of spectral and spatial features, a novel feature fusion strategy is proposed to fuse spectral and spatial features extracted by a two-stream deep convolutional autoencoder network. Finally, a band attention module is used to automatically learn small weights for redundant bands and thus reduce the negative effect caused by redundant bands. Experimental results on five public datasets demonstrate the superiority of the proposed method compared to several state-of-the-art HAD methods in the detection performance.

Background information extraction and modeling have always been the cores of hyperspectral anomaly detection (AD) algorithms. In this letter, a simple and efficient weighting strategy based on image segmentation is proposed. The strategy integrates detection results with spatial information from segmentation, and the background is suppressed in the fusion results. First, an improved graph-based image-segmentation algorithm is adopted to isolate potential anomaly targets from the background. The segmentation is based on the spectral similarity of adjacent pixels and can extract the potential target well even if the global background is complex. Then, a weight matrix is constructed according to the segmentation result, and two types of background, narrow boundaries and large homogeneous areas, are suppressed and assigned small weights. Finally, the normalized weight matrix is combined with the detection results of AD algorithms. Experiments conducted on different data sets show that the proposed strategy is efficient and robust and can improve the detection performance and robustness of AD algorithms.

In this paper, we propose a novel sparsity-based algorithm for anomaly detection in hyperspectral imagery. The algorithm is based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood. To be physically meaningful, the sum-to-one and nonnegativity constraints are imposed to abundance vector based on the linear mixture model, and the upper bound constraint on sparsity level is removed for better recovery of the test pixel. First, the proposed method utilizes the redundant background information to automatically remove anomalies from the background dictionary. Then, the reconstruction error obtained by the new background dictionary is directly used for anomaly detection. Moreover, a kernel version of the proposed method is also derived to completely exploit the nonlinear feature of hyperspectral data. An important advantage of the proposed methods is their capability to adaptively model the background even when some anomaly pixels are involved. Extensive experiments have been conducted on three real hyperspectral data sets. It is demonstrated that the proposed detectors achieve a promising detection performance with a relatively low computational cost.