Multichannel image completion with mixture noise is a challenging problem in the fields of machine learning, computer vision, image processing, and data mining. Traditional image completion models are not appropriate to deal with this problem directly since their reconstruction priors may mismatch corruption priors. To address this issue, we propose a novel nonlocal self-similarity-based weighted tensor low-rank decomposition (NSWTLD) model that can achieve global optimization and local enhancement. In the proposed model, based on the corruption priors and the reconstruction priors, a pixel weighting strategy is given to characterize the joint effects of missing data, the Gaussian noise, and the impulse noise. To discover and utilize the accurate nonlocal self-similarity information to enhance the restoration quality of the details, the traditional nonlocal learning framework is optimized by employing improved index determination of patch group and handling strip noise caused by patch overlapping. In addition, an efficient and convergent algorithm is presented to solve the NSWTLD model. Comprehensive experiments are conducted on four types of multichannel images under various corruption scenarios. The results demonstrate the efficiency and effectiveness of the proposed model.

Hyperspectral image mixed denoising is a challenging task in the fields of remote sensing, environmental monitoring, mineral exploration, etc. A crucial difficulty is to acquire clean restoration from hyperspectral image (HSI) that encounters Gaussian noise, impulse noise, strip noise and deadlines. In the previous works, combining global information and nonlocal information is a popular way to learn the comprehensive characteristics of the clean HSI. However, the advantages of 2D spatial structure similarity and spectral low-rankness may not be fully exploited at the same time in global prior learning. The iterative update between global restoration and nonlocal restoration may cause high time consumption and certain loss of information. To address these issues, we propose a three-stage mixed denoising model based on novel hybrid low-rank tensor approximation and global-guided-nonlocal prior mechanism (HLTA-GN). Firstly, to learn a good global prior, hybrid low-rank tensor approximation incorporated with a useful nonconvex tensor rank estimation is presented to balance 2D spatial similarity and spectral low-rankness. Secondly, to learn a high-quality nonlocal prior, global-guided-nonlocal prior mechanism is proposed to help nonlocal restoration suppress the residual noise. At the same time, a regularized sequential low-rank tensor approximation is proposed to enhance the robustness to noisy patch groups. Thirdly, a weighted fusion on global prior and nonlocal prior helps to further balance global denoising and patch processing. An efficient learning algorithm is provided to solve HLTA-GN. Abundant experiments are conducted on various HSIs with several scenarios. The experimental results demonstrate the superiority of HLTA-GN.

Multichannel image completion with mixture noise is a common but complex problem in the fields of machine learning, image processing, and computer vision. Most existing algorithms devote to explore global low-rank information and fail to optimize local and joint-mode structures, which may lead to oversmooth restoration results or lower quality restoration details. In this study, we propose a novel model to deal with multichannel image completion with mixture noise based on adaptive sparse low-rank tensor subspace and nonlocal self-similarity (ASLTS-NS). In the proposed model, a nonlocal similar patch matching framework cooperating with Tucker decomposition is used to explore information of global and joint modes and optimize the local structure for improving restoration quality. In order to enhance the robustness of low-rank decomposition to data missing and mixture noise, we present an adaptive sparse low-rank regularization to construct robust tensor subspace for self-weighing importance of different modes and capturing a stable inherent structure. In addition, joint tensor Frobenius and l₁ regularizations are exploited to control two different types of noise. Based on alternating directions method of multipliers (ADMM), a convergent learning algorithm is designed to solve this model. Experimental results on three different types of multichannel image sets demonstrate the advantages of ASLTS-NS under five complex scenarios.

Cross-scene hyperspectral image (HSI) classification plays a vital role in a variety of applications, including agricultural monitoring, military reconnaissance, and urban planning. Due to the nonlinear nature of land-cover characteristics, the diversity of scenes, and the distribution shifts caused by variations in acquisition conditions or sensors, deep HSI open-set domain adaptation (OSDA) has emerged as a key approach to tackling cross-scene HSI classification. Existing methods assume identical joint distributions between training and test data. However, this rigorous assumption renders them ineffective in real-world scenes when encountering varied sensors, changed imaging conditions, different spatial resolution in HSI imaging, e.g., water vapor bands at 1.36-1.39 μm. To address this, we propose a more generalizable deep HSI OSDA method JDMUS based on Joint Distribution Matching and unknown class Uncertainty Suppression, for cross-scene HSI classification. Specifically, we employ χ²-divergence to characterize the joint distribution discrepancy and propose a least-squares χ²-divergence estimator without explicitly modeling the two joint distributions, yielding a quadratic problem with an analytical solution. Furthermore, we prove the methodology that the upper bound of the target error for neural network classification model is determined by the average source error, χ²-divergence, and open-set risk difference. Based on this methodology, we design joint distribution matching and unknown class uncertainty suppression mechanisms, and thus construct the JDMUS algorithm with theoretical guarantees, in which weighted generative adversarial network with dynamic thresholding is used for feature extraction. Experiments on cross-scene HSI datasets demonstrate that our JDMUS outperforms existing state-of-the-art approaches.