Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \( \mathcal{O}(N^2d) \) to \( \mathcal{O}(Nr^2) \), with \( r

Pansharpening technique is dedicated to enriching the spatial details of low-resolution multispectral images (LRMS) under the guidance of a panchromatic (PAN) image. With the guarantee of promising results, Transformer-based methods have enjoyed a high reputation in this field. However, to reduce computational cost, existing solutions typically divide images into smaller, independent windows, which often weakens inter-window and channel-wise interactions as well as leads to unsmooth edges. To address these issues, we first formulate the pansharpening task as a variational optimization problem, and subsequently solve its data and prior subproblems alternately through an unrolling algorithm. In the prior extractor, we propose a Pipeline-Centered Neighboring Attention (PCNA), which holistically allows all pixels to share the same attention span while fully leveraging channel dependencies, thereby significantly improving the capability to process multispectral images. Moreover, a Multi-Scale Channel-Aware (MSCA) module is designed to capture the edges and structural details. Finally, by sequentially integrating the data and prior modules at each iteration stage, we unroll the iterations into a stage-wise unfolding network. Extensive experiments on three satellite datasets demonstrate the effectiveness and efficiency of our proposal compared to cutting-edge methods.

In recent years, camouflage object detection has remained a significant challenge due to the high similarities between objects and backgrounds. Relying solely on convolutions with limited receptive fields or attentions with fixed ranges is in trouble with handling the size variability of cared objects. Moreover, camouflaged targets are frequently covered by their surroundings, with existing methods prone to erroneously identifying the occluded portions. To break the dilemma, we propose a multi-scale core-peripheral attention network (CPANet), mainly including two elaborations: core-peripheral mask attention (CPMA) and multi-scale weighted fusion (MSWF). CPMA boosts camouflaged features by employing core- and peripheral-based attention mechanisms, mitigating the influence of surrounding obstacles and enabling precise localization of concealed targets. Additionally, MSWF captures multi-scale low-level features to refine local details and manifest complete object representations. Extensive evaluations demonstrate that CPANet outperforms state-of-the-art methods across four widely used benchmarks.

Due to the acquisition limitations of physical devices, pansharpening serves as a computational alternative, enhancing spatial details in low-resolution hyperspectral images with the guidance of corresponding panchromatic images. By leveraging the benefits of nonlinear network architectures and interpretable optimization schemes, deep unfolding networks (DUNs) have shed new light on pansharpening. However, current DUNs lack a dedicated design for both estimating the degradation matrices and extracting intricate information from the proximal operator. To address these challenges, we propose a novel Collaborative Cross-Complementary Unfolding Network (C3U), which is organized into two main steps: customized multi-scale convolution estimation (MSCE) and a data-driven prior extractor. In the MSCE step, the spatial and spectral degradation matrices are individually adapted through multiscale treatment and point convolution operations. Specifically, the overall estimation undergoes an end-to-end iterative block, allowing for adaptive modeling of complex spatial and spectral structures. Within the prior extractor, a cross-complementary attention mechanism is proposed to enable iterative information interaction between global and local Transformers, capturing holistic features and enhancing inductive capacity. Additionally, a collaborative scale-aware-channel mechanism is designed to enlarge the receptive field and capture multiscale channel features in a lightweight manner. More importantly, the principle of collaborative cross-complementary (CCC) permeates all the sub-assemblies, ensuring a desirable information flow. Experimental results on multiple remote sensing datasets demonstrate the superiority of the proposed method over previous state-of-the-art (SOTA) techniques, achieving a 0.8 dB PSNR gain on the GF-2 dataset.

Tensor Nuclear Norm (TNN) has been widely employed as a regularization term for hyperspectral image super-resolution (HSISR). However, conventional TNN constraints based on Discrete Fourier Transform (DFT) often suffer from rank estimation biases and an inability to effectively capture complex spectral-spatial correlations, limiting their efficacy in HSISR. To address these challenges, we propose a Nonlinear Learnable Triple-domain (NLT) transform framework that integrates nonlinear transform, DFT, and self-learning adaptation. This multi-stage process promotes singular value concentration, improving low-rank approximation and rank estimation accuracy. Building upon this framework, we develop an NL-transform-oriented tensor product, a truncated singular value decomposition (TSVD) operation, and a novel tensor nuclear norm (NLTN) tailored for HSISR. By incorporating spectral subspace estimation and clustering-based patch grouping, our approach effectively leverages spatial-spectral correlations and non-local self-similarities, leading to enhanced reconstruction quality. To further mitigate singular value over-penalization, we introduce a logarithmic-based generalized NLTNN (GNLTN) and formulate an optimization strategy based on the alternating direction method of multipliers (ADMM). Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of fusion accuracy and visual fidelity, setting new benchmarks for hyperspectral image super-resolution. The code is available at https://github.com/xuhonghui96/GNLTN.

In recent studies, tensor singular value decomposition (TSVD) within the high-order (Ho) algebra has shed light on solving the Tensor Robust Principal Component Analysis (TRPCA) problem. However, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimal outcomes. To overcome this limitation, we propose a self-learning TSVD method that rectifies computational inefficiencies and learns a lossless transformation, inducing a lower average-rank tensor. This involves multiplying learnable semi-orthogonal matrices obtained through Tucker compression with the original tensor along all modes, resulting in a core tensor with enhanced inherent low rankness and new self-learning transform matrices. The semi-orthogonal transforms, acting as a crucial building block, enhance spatial low-rankness, facilitating the resolution of smaller-scale problems and the design of efficient algorithms. Additionally, a reweighting Schatten-p scheme is integrated into the self-learning HoTSVD to understand global low-rank correlations, offering an effective numerical solution. Finally, we develop an alternating direction method of multipliers (ADMM)-based algorithm as a solver. Experimental results on Light Field Images (LFI), showcase the superiority of our proposed method over previous state-of-the-art approaches.

Recent advancements in tensor nuclear norm (TNN) have led to promising solutions for hyperspectral image super-resolution (HSISR), which produces enriched outputs by fusing low-resolution hyperspectral images (LRHSIs) with high-resolution multispectral images (HRMSIs). However, current TNN, mainly reliant on the discrete Fourier transform (DFT), still suffers from mirroring boundary effects and singleton domain limitation. As a relief, we propose cascade-transform-based tensor nuclear norm (CTNN) with two variants for HSISR, featuring new definitions and algebraic structures for tensor product and TNN operations. The first variant processes tubal elements derived from DFT as inputs in the discrete cosine transform (DCT) domain, allowing for more nuanced feature extraction. The second learns adaptive matrices from the data in each iteration update and links them with a fixed DFT matrix to dynamically update the transform domain, preventing rank estimation bias. Furthermore, the nonconvex form of the proposed CTNN is applied to three modes of each spectral subspace similarity cube, termed log-sum-based full-scale CTNN (LFCTNN), capturing the global low-rank structure of LRHSI and the nonlocal similarities present in HRMSI. Experimental evaluations on various remote sensing datasets indicate that our approach exceeds existing state-of-the-art methods. The code is available athttps://github.com/xuhonghui96/LFCTNN.