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SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
Fengchao Xiong; Jun Zhou; Shuyin Tao; Jianfeng Lu; Jiantao Zhou; Yuntao Qian
2022-08
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
Pages5469-5483
Abstract

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.

KeywordHyperspectral Image Denoising Model-based Neural Network Low-rank Representation Multidimensional Sparse Representation
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorJiantao Zhou
Recommended Citation
GB/T 7714
Fengchao Xiong,Jun Zhou,Shuyin Tao,et al. SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising[J]. IEEE Transactions on Image Processing,2022:5469-5483.
APA Fengchao Xiong,Jun Zhou,Shuyin Tao,Jianfeng Lu,Jiantao Zhou,&Yuntao Qian.(2022).SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising.IEEE Transactions on Image Processing,5469-5483.
MLA Fengchao Xiong,et al."SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising".IEEE Transactions on Image Processing (2022):5469-5483.
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