Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between noisy and clean HSI pairs. They usually do not consider the physical characteristics of HSIs. This drawback makes the models lack interpretability that is key to understanding their denoising mechanism and limits their denoising ability. In this paper, we introduce a novel model-guided interpretable network for HSI denoising to tackle this problem. Fully considering the spatial redundancy, spectral low-rankness, and spectral-spatial correlations of HSIs, we first establish a subspace-based multidimensional sparse (SMDS) model under the umbrella of tensor notation. After that, the model is unfolded into an end-to-end network named SMDS-Net, whose fundamental modules are seamlessly connected with the denoising procedure and optimization of the SMDS model. This makes SMDS-Net convey clear physical meanings, i.e., learning the low-rankness and sparsity of HSIs. Finally, all key variables are obtained by discriminative training. Extensive experiments and comprehensive analysis on synthetic and real-world HSIs confirm the strong denoising ability, strong learning capability, promising generalization ability, and high interpretability of SMDS-Net against the state-of-the-art HSI denoising methods. The source code and data of this article will be made publicly available at https://github.com/bearshng/smds-net for reproducible research.

中文翻译：

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SMDS-Net：用于高光谱图像去噪的模型引导光谱空间网络

基于深度学习 (DL) 的高光谱图像 (HSI) 去噪方法直接学习噪声和干净 HSI 对之间的非线性映射。他们通常不考虑 HSI 的物理特性。这个缺点使模型缺乏可解释性，这是理解其去噪机制的关键，并限制了它们的去噪能力。在本文中，我们介绍了一种用于 HSI 去噪的新型模型引导可解释网络来解决这个问题。充分考虑 HSI 的空间冗余、谱低秩和谱空间相关性，我们首先在张量符号的保护下建立了基于子空间的多维稀疏 (SMDS) 模型。之后，模型展开成一个名为 SMDS-Net 的端到端网络，其基本模块与去噪过程和SMDS模型的优化无缝连接。这使得 SMDS-Net 传达了清晰的物理意义，即学习 HSI 的低秩和稀疏性。最后，通过判别训练获得所有关键变量。对合成和真实世界 HSI 的广泛实验和综合分析证实了 SMDS-Net 与最先进的 HSI 去噪方法相比具有强大的去噪能力、强大的学习能力、有希望的泛化能力和高可解释性。本文的源代码和数据将在 对合成和真实世界 HSI 的广泛实验和综合分析证实了 SMDS-Net 与最先进的 HSI 去噪方法相比具有强大的去噪能力、强大的学习能力、有希望的泛化能力和高可解释性。本文的源代码和数据将在 对合成和真实世界 HSI 的广泛实验和综合分析证实了 SMDS-Net 与最先进的 HSI 去噪方法相比具有强大的去噪能力、强大的学习能力、有希望的泛化能力和高可解释性。本文的源代码和数据将在https://github.com/bearshng/smds-net用于可重复的研究。