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Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization
Xie, Yuan1,2; Qu, Yanyun3; Tao, Dacheng4; Wu, Weiwei3; Yuan, Qiangqiang5; Zhang, Wensheng2
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
2016-08-01
Volume54Issue:8Pages:4642-4659
SubtypeArticle
AbstractHyperspectral images (HSIs) are inevitably corrupted by mixture noise during their acquisition process, in which various kinds of noise, e.g., Gaussian noise, impulse noise, dead lines, and stripes, may exist concurrently. In this paper, mixture noise removal is well illustrated by the task of recovering the low-rank and sparse components of a given matrix, which is constructed by stacking vectorized HSI patches from all the bands at the same position. Instead of applying a traditional nuclear norm, a nonconvex low-rank regularizer, i.e., weighted Schatten p-norm (WSN), is introduced to not only give better approximation to the original low-rank assumption but also to consider the importance of different rank components. The resulted nonconvex low-rank matrix approximation (LRMA) model falls into the applicable scope of an augmented Lagrangian method, and its WSN minimization subproblem can be efficiently solved by generalized iterated shrinkage algorithm. Moreover, the proposed model is integrated into an iterative regularization schema to produce final results, leading to a completed HSI restoration framework. Extensive experimental testing on simulated and real data shows, both qualitatively and quantitatively, that the proposed method has achieved highly competent objective performance compared with several state-of-the-art HSI restoration methods.
KeywordHyperspectral Image (Hsi) Low-rank Matrix Approximation (Lrma) Restoration Weighted Schatten P-norm (Wsn)
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2016.2547879
WOS KeywordRANK MATRIX RECOVERY ; SPARSE REPRESENTATION ; COMPONENT ANALYSIS ; ALGORITHM ; DOMAIN ; SHRINKAGE
Indexed BySCI
Language英语
Funding OrganizationHong Kong Scholars Program ; National Natural Science Foundation of China(61402480 ; Australian Research Council(DP-120103730 ; 61432008 ; FT-130101457) ; 61472423 ; 61502495 ; 41401383 ; 61373077)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000381434600023
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10722
Collection精密感知与控制研究中心_人工智能与机器学习
Affiliation1.Hong Kong Polytech Univ, Dept Comp, Visual Comp Lab, Kowloon, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
3.Xiamen Univ, Dept Comp Sci, Video & Image Lab, Xiamen 361005, Peoples R China
4.Univ Technol, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia
5.Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China
Recommended Citation
GB/T 7714
Xie, Yuan,Qu, Yanyun,Tao, Dacheng,et al. Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2016,54(8):4642-4659.
APA Xie, Yuan,Qu, Yanyun,Tao, Dacheng,Wu, Weiwei,Yuan, Qiangqiang,&Zhang, Wensheng.(2016).Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,54(8),4642-4659.
MLA Xie, Yuan,et al."Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 54.8(2016):4642-4659.
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