Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and glo...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 58; H. 2; S. 1348 - 1362
Hauptverfasser:	Chen, Yong, He, Wei, Yokoya, Naoto, Huang, Ting-Zhu, Zhao, Xi-Le
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation Computer simulation Correlation Data models Datasets Decomposition Denoising Dimensions hyperspectral image (HSI) Hyperspectral imaging Image processing Matrix decomposition Methods Noise levels Noise reduction nonlocal self-similarity Optimization Performance evaluation Remote sensing Self-similarity Spectral correlation tensor-ring (TR) decomposition Tensors
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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