一种受TRPCA启发的深度展开网络：基于阈值化t-SVD与Top-K稀疏Transformer的高光谱图像去噪
A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer
AI总结：
本文提出受TRPCA启发的深度展开网络DU-TRPCA，通过阈值化t-SVD低秩模块与Top-K稀疏Transformer模块的逐阶段紧耦合交替，在保持可解释性的同时有效去除高光谱图像中的复杂混合噪声，并在严重噪声条件下超越现有方法。
摘要：
高光谱图像（HSIs）在采集和传输过程中常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。近年来，桥接模型驱动与数据驱动范式的混合方法展现出巨大潜力。然而，大多数这类方法缺乏在不同先验或模块之间的有效交替，导致正则化耦合松散，未能充分利用其互补优势。受张量鲁棒主成分分析（TRPCA）启发，我们提出了一种新颖的深度展开网络（DU-TRPCA），该网络在低秩模块与稀疏模块这两个紧密集成的模块之间执行逐阶段交替。低秩模块采用阈值化张量奇异值分解（t-SVD），为张量低秩性提供了一种被广泛采用的凸替代，并已被证明能够有效捕获HSIs的全局空间-光谱结构。Top-K稀疏Transformer模块自适应地施加稀疏约束，直接匹配TRPCA中的稀疏正则化，能够有效去除局部异常值和复杂噪声。这种紧耦合架构保留了TRPCA中固有的低秩逼近与稀疏细化之间的逐阶段交替特性，同时通过注意力机制增强了表示能力。在合成和真实HSIs上的大量实验表明，DU-TRPCA在严重混合噪声下超越了最先进的方法，同时具备受迭代优化启发的可解释性优势和稳定的去噪动态特性。代码可在https://github.com/liangli97/TRPCA-Deep-Unfolding-HSI-Denoising获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
