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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3196826
|2 doi
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|a pubmed24n1148.xml
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Xiong, Fengchao
|e verfasserin
|4 aut
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| 245 |
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|a SMDS-Net
|b Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 18.08.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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
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|a Journal Article
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|a Zhou, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tao, Shuyin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Jianfeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Jiantao
|e verfasserin
|4 aut
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| 700 |
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|a Qian, Yuntao
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 23., Seite 5469-5483
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:31
|g year:2022
|g day:23
|g pages:5469-5483
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2022.3196826
|3 Volltext
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