Hyperspectral Image Restoration via Global L1-2 Spatial-Spectral Total Variation Regularized Local Low-Rank Tensor Recovery

Hyperspectral images (HSIs) are usually corrupted by various noises, e.g., Gaussian noise, impulse noise, stripes, dead lines, and many others. In this article, motivated by the good performance of the <inline-formula> <tex-math notation="LaTeX">L_{1-2} </tex-math></in...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 59; no. 4; pp. 3309 - 3325
Main Authors:	Zeng, Haijin, Xie, Xiaozhen, Cui, Haojie, Yin, Hanping, Ning, Jifeng
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Exploitation Hyperspectral images (HSIs) Hyperspectral imaging Hyperspectral sensors Image restoration Iterative methods L1-2 spatial–spectral total variation (L1-2SSTV) local low-rank tensor recovery (LTR) Mathematical analysis mixed noise Noise Noise reduction Random noise Recovery Regularization Restoration Singular value decomposition Solid modeling Sparse matrices Tensile stress Tensors
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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