Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial–spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell _1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the nonlocal total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.

The L ₁ -norm low-rank matrix factorization (LRMF) has been attracting much attention due to its wide applications to computer vision and pattern recognition. In this paper, we construct a new hierarchical Bayesian generative model for the L ₁ -norm LRMF problem and design a mean-field variational method to automatically infer all the parameters involved in the model by closed-form equations. The variational Bayesian inference in the proposed method can be understood as solving a weighted LRMF problem with different weights on matrix elements based on their significance and with L ₂ -regularization penalties on parameters. Throughout the inference process of our method, the weights imposed on the matrix elements can be adaptively fitted so that the adverse influence of noises and outliers embedded in data can be largely suppressed, and the parameters can be appropriately regularized so that the generalization capability of the problem can be statistically guaranteed. The robustness and the efficiency of the proposed method are substantiated by a series of synthetic and real data experiments, as compared with the state-of-the-art L ₁ -norm LRMF methods. Especially, attributed to the intrinsic generalization capability of the Bayesian methodology, our method can always predict better on the unobserved ground truth data than existing methods.

In this letter, we consider the problem of compressive sensing of hyperspectral images (HSIs). We propose a novel tensor-based approach by modeling the global spatial–spectral correlation and local smoothness properties hidden in HSIs. Specifically, we use the tensor Tucker decomposition to describe the global spatial–spectral correlation among all HSI bands, and a weighted 3-D total variation to characterize the local smooth structure in both spatial and spectral modes. We then design an efficient algorithm to solve the resulting optimization problem by using the alternating direction method of multipliers. Experimental results on several HSI data sets demonstrate improved reconstruction performance of the proposed approach, as compared with other competing approaches.

Low-light image enhancement (LIE) has attracted tremendous research interests in recent years. Retinex theory-based deep learning methods, following a decomposition-adjustment pipeline, have achieved promising performance due to their physical interpretability. However, existing Retinex-based deep learning methods are still suboptimal, failing to leverage useful insights from traditional approaches. Meanwhile, the adjustment step is either oversimplified or overcomplicated, resulting in unsatisfactory performance in practice. To address these issues, we propose a novel deep-learning framework for LIE. The framework consists of a decomposition network (DecNet) inspired by algorithm unrolling and adjustment networks considering both global and local brightness. The algorithm unrolling allows the integration of both implicit priors learned from data and explicit priors inherited from traditional methods, facilitating better decomposition. Meanwhile, considering global and local brightness guides the design of effective yet lightweight adjustment networks. Moreover, we introduce a self-supervised fine-tuning strategy that achieves promising performance without manual hyperparameter tuning. Extensive experiments on benchmark LIE datasets demonstrate the superiority of our approach over existing state-of-the-art methods both quantitatively and qualitatively. Code is available at https://github.com/Xinyil256/RAUNA2023 .

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC_matlab_2020 .