Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization

doi:10.1109/TGRS.2016.2547879

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p-Norm Minimization
	Xie, Yuan1,2 ; Qu, Yanyun 3; Tao, Dacheng 4; Wu, Weiwei 3; Yuan, Qiangqiang 5; Zhang, Wensheng2
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2016-08-01
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
DOI	10.1109/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	被引频次：127[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/10722
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
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
第一作者单位	精密感知与控制研究中心
推荐引用方式 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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