Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/609047
Title: Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p Norm Minimization
Authors: Yuan Xie;Yanyun Qu;Dacheng Tao;Weiwei Wu;Qiangqiang Yuan;Wensheng Zhang
subject: weighted Schatten $p$-norm (WSN)|low-rank matrix approximation (LRMA)|restoration|Hyperspectral image (HSI)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/140721
http://localhost/handle/Hannan/609047
ISSN: 0196-2892
1558-0644
volume: 54
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7453172.pdf10.93 MBAdobe PDFThumbnail
Preview File
Title: Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p Norm Minimization
Authors: Yuan Xie;Yanyun Qu;Dacheng Tao;Weiwei Wu;Qiangqiang Yuan;Wensheng Zhang
subject: weighted Schatten $p$-norm (WSN)|low-rank matrix approximation (LRMA)|restoration|Hyperspectral image (HSI)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/140721
http://localhost/handle/Hannan/609047
ISSN: 0196-2892
1558-0644
volume: 54
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7453172.pdf10.93 MBAdobe PDFThumbnail
Preview File
Title: Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p Norm Minimization
Authors: Yuan Xie;Yanyun Qu;Dacheng Tao;Weiwei Wu;Qiangqiang Yuan;Wensheng Zhang
subject: weighted Schatten $p$-norm (WSN)|low-rank matrix approximation (LRMA)|restoration|Hyperspectral image (HSI)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/140721
http://localhost/handle/Hannan/609047
ISSN: 0196-2892
1558-0644
volume: 54
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7453172.pdf10.93 MBAdobe PDFThumbnail
Preview File