Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2017-12, Vol.9 (12), p.1286
Main Authors: Wang, Yao, Chen, Xi’ai, Han, Zhi, He, Shiying
Format: Article
Language:English
Subjects:
ADMM
Algorithms
Approximation
folded-concave regularization
hyperspectral image super-resolution
Image resolution
low-rank tensor approximation
Mathematical analysis
Noise
nonlocal self-similarity
Optimization
Regularization
Remote sensing
Remote sensing systems
Self-similarity
Smoothness
Spectra
Tensors
total variation
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Summary:Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9121286