Paper Info
Title
Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction.
Abstract
Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the l(1)-based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.
Year
DOI
Venue
2019
10.3390/rs11020193
REMOTE SENSING
Keywords
Field
DocType
hyperspectral image,compressive sensing,structured sparsity,tensor sparse decomposition,tensor low-rank approximation
ENCODE,Computer vision,Tensor,Sparse approximation,Algorithm,Matrix norm,Hyperspectral imaging,Regularization (mathematics),Artificial intelligence,Geology,Compressed sensing,Cube
Journal
Volume
Issue
Citations 
11
2
8
PageRank 
References 
Authors
0.45
36
4
Name
Order
Citations
PageRank
Jize Xue1143.21
Yongqiang Zhao230724.84
Wenzhi Liao340331.63
Jonathan Cheung-Wai Chan415518.46