Title
Hyperspectral Image Recovery Using Nonconvex Sparsity and Low-Rank Regularizations
Abstract
Hyperspectral image (HSI) restoration is an important preprocessing step in HSI data analysis to improve the image quality for subsequent applications of HSI. In this article, we introduce a spatial–spectral patch-based nonconvex sparsity and low-rank regularization method for HSI restoration. In contrast to traditional approaches based on convex penalties or nonconvex spectral penalty alone, we consider the sparsity of HSI in the spatial–spectral domain and combine the nonconvex low-rank penalty and the nonconvex 3-D total variation (TV)-like sparsity regularization to fully exploit the correlations in both spatial–spectral dimensions of the HSI data set. In addition, we propose a fast iterative variable splitting-based algorithm to effectively solve the corresponding optimization problem. Numerical experiments on both simulated and real HSI data sets demonstrate that the proposed nonconvex low-rank and TV (NonLRTV) method significantly improves the recovered image quality compared with the state-of-the-art algorithms.
Year
DOI
Venue
2020
10.1109/TGRS.2019.2937901
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
TV,Image restoration,Hyperspectral imaging,Correlation,Noise reduction,Optimization
Computer vision,Hyperspectral imaging,Artificial intelligence,Image recovery,Mathematics
Journal
Volume
Issue
ISSN
58
1
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Yue Hu1112.89
Xiaodi Li200.34
Yanfeng Gu374255.56
M. Jacob4123.85