标题： 基于TRPCA启发的深度展开网络通过阈值t-SVD和Top-K稀疏Transformer进行高光谱图像去噪 显示英文标题

标题： A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer

摘要： 高光谱图像（HSIs）在采集和传输过程中经常受到复杂混合噪声的退化，因此有效的去噪对于后续分析至关重要。最近结合模型驱动和数据驱动范式的混合方法显示出巨大的潜力。然而，大多数这些方法缺乏不同先验或模块之间的有效交替，导致正则化松散耦合且未能充分挖掘它们的互补优势。 受张量鲁棒主成分分析（TRPCA）的启发，我们提出了一种新颖的深度展开网络（DU-TRPCA），该网络强制在两个紧密集成的模块之间进行阶段式交替：低秩和稀疏。低秩模块采用阈值化的张量奇异值分解（t-SVD），提供了一个广泛采用的张量低秩性凸代理，并已被证明能够有效捕捉高光谱图像的空间-光谱全局结构。Top-K稀疏变换器模块自适应地施加稀疏约束，直接与TRPCA中的稀疏正则化相匹配，从而实现对局部异常值和复杂噪声的有效去除。 这种紧密耦合的架构保留了TRPCA中固有的低秩逼近和稀疏细化之间的阶段式交替，同时通过注意力机制增强了表征能力。在合成和真实高光谱图像上的大量实验表明，DU-TRPCA在严重混合噪声下优于最先进的方法，同时提供了由迭代优化启发的可解释性和稳定的去噪动态。代码可在https://github.com/liangli97/TRPCA-Deep-Unfolding-HSI-Denoising获取。 显示英文摘要

摘要： Hyperspectral images (HSIs) are often degraded by complex mixed noise during acquisition and transmission, making effective denoising essential for subsequent analysis. Recent hybrid approaches that bridge model-driven and data-driven paradigms have shown great promise. However, most of these approaches lack effective alternation between different priors or modules, resulting in loosely coupled regularization and insufficient exploitation of their complementary strengths. Inspired by tensor robust principal component analysis (TRPCA), we propose a novel deep unfolding network (DU-TRPCA) that enforces stage-wise alternation between two tightly integrated modules: low-rank and sparse. The low-rank module employs thresholded tensor singular value decomposition (t-SVD), providing a widely adopted convex surrogate for tensor low-rankness and has been demonstrated to effectively capture the global spatial-spectral structure of HSIs. The Top-K sparse transformer module adaptively imposes sparse constraints, directly matching the sparse regularization in TRPCA and enabling effective removal of localized outliers and complex noise. This tightly coupled architecture preserves the stage-wise alternation between low-rank approximation and sparse refinement inherent in TRPCA, while enhancing representational capacity through attention mechanisms. Extensive experiments on synthetic and real-world HSIs demonstrate that DU-TRPCA surpasses state-of-the-art methods under severe mixed noise, while offering interpretability benefits and stable denoising dynamics inspired by iterative optimization. Code is available at https://github.com/liangli97/TRPCA-Deep-Unfolding-HSI-Denoising.