Remote Sensing | |
Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization | |
Yao Wang1  Shiying He1  Xi’ai Chen2  Zhi Han2  | |
[1] School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China;State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; | |
关键词: hyperspectral image super-resolution; low-rank tensor approximation; nonlocal self-similarity; folded-concave regularization; total variation; ADMM; | |
DOI : 10.3390/rs9121286 | |
来源: DOAJ |
【 摘 要 】
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.
【 授权许可】
Unknown