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

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...

Full description

Bibliographic Details
Main Authors: Yao Wang, Xi’ai Chen, Zhi Han, Shiying He
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Remote Sensing
Subjects:
hyperspectral image super-resolution
low-rank tensor approximation
nonlocal self-similarity
folded-concave regularization
total variation
ADMM
Online Access:https://www.mdpi.com/2072-4292/9/12/1286

Internet

https://www.mdpi.com/2072-4292/9/12/1286

Similar Items