标题： 迭代低秩网络用于高光谱图像去噪 显示英文标题

标题： Iterative Low-rank Network for Hyperspectral Image Denoising

摘要： 高光谱图像（HSI）去噪是后续任务的重要预处理步骤。 干净的HSI通常位于低维子空间中，可以通过低秩和稀疏表示来捕捉，这被称为HSI的物理先验。 在有效去噪的同时充分使用这些物理特性以保留图像细节通常具有挑战性。 本文引入了一种新颖的迭代低秩网络（ILRNet）来解决这些挑战。 ILRNet通过在U-Net架构中嵌入一个秩最小化模块（RMM），结合了模型驱动和数据驱动方法的优势。 该模块在前向传播过程中将特征图转换到小波域，并对低频成分应用奇异值阈值处理（SVT），利用了特征域中HSI的光谱低秩性。 该参数与奇异向量阈值算法的超参数密切相关，从数据中自适应学习，从而能够在不同场景下灵活有效地捕捉低秩性。 此外，ILRNet具有一个迭代细化过程，自适应地将中间去噪的HSI与噪声输入结合。 这种方式确保了逐步增强和优越的图像细节保留。 实验结果表明，ILRNet在合成和真实世界噪声去除任务中均达到了最先进的性能。 显示英文摘要

摘要： Hyperspectral image (HSI) denoising is a crucial preprocessing step for subsequent tasks. The clean HSI usually reside in a low-dimensional subspace, which can be captured by low-rank and sparse representation, known as the physical prior of HSI. It is generally challenging to adequately use such physical properties for effective denoising while preserving image details. This paper introduces a novel iterative low-rank network (ILRNet) to address these challenges. ILRNet integrates the strengths of model-driven and data-driven approaches by embedding a rank minimization module (RMM) within a U-Net architecture. This module transforms feature maps into the wavelet domain and applies singular value thresholding (SVT) to the low-frequency components during the forward pass, leveraging the spectral low-rankness of HSIs in the feature domain. The parameter, closely related to the hyperparameter of the singular vector thresholding algorithm, is adaptively learned from the data, allowing for flexible and effective capture of low-rankness across different scenarios. Additionally, ILRNet features an iterative refinement process that adaptively combines intermediate denoised HSIs with noisy inputs. This manner ensures progressive enhancement and superior preservation of image details. Experimental results demonstrate that ILRNet achieves state-of-the-art performance in both synthetic and real-world noise removal tasks.