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Remote Sens. 2017, 9(12), 1286; doi:10.3390/rs9121286

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

1
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
2
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 29 September 2017 / Revised: 7 December 2017 / Accepted: 7 December 2017 / Published: 11 December 2017
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
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Abstract

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. View Full-Text
Keywords: hyperspectral image super-resolution; low-rank tensor approximation; nonlocal self-similarity; folded-concave regularization; total variation; ADMM hyperspectral image super-resolution; low-rank tensor approximation; nonlocal self-similarity; folded-concave regularization; total variation; ADMM
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Wang, Y.; Chen, X.; Han, Z.; He, S. Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization. Remote Sens. 2017, 9, 1286.

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