Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization
Xie, Yuan1,2; Qu, Yanyun3; Tao, Dacheng4; Wu, Weiwei3; Yuan, Qiangqiang5; Zhang, Wensheng2
2016-08-01
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷号54期号:8页码:4642-4659
文章类型Article
摘要Hyperspectral 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.
关键词Hyperspectral Image (Hsi) Low-rank Matrix Approximation (Lrma) Restoration Weighted Schatten P-norm (Wsn)
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2016.2547879
关键词[WOS]RANK MATRIX RECOVERY ; SPARSE REPRESENTATION ; COMPONENT ANALYSIS ; ALGORITHM ; DOMAIN ; SHRINKAGE
收录类别SCI
语种英语
项目资助者Hong Kong Scholars Program ; National Natural Science Foundation of China(61402480 ; Australian Research Council(DP-120103730 ; 61432008 ; FT-130101457) ; 61472423 ; 61502495 ; 41401383 ; 61373077)
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000381434600023
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10722
专题精密感知与控制研究中心_人工智能与机器学习
作者单位1.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
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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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