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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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 58; no. 2; pp. 1348 - 1362
Main Authors:	Chen, Yong, He, Wei, Yokoya, Naoto, Huang, Ting-Zhu, Zhao, Xi-Le
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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