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

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 recov...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2016-08, Vol.54 (8), p.4642-4659
Main Authors: Xie, Yuan, Qu, Yanyun, Tao, Dacheng, Wu, Weiwei, Yuan, Qiangqiang, Zhang, Wensheng
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Gaussian noise
Hyperspectral image (HSI)
Image restoration
low-rank matrix approximation (LRMA)
Mathematical analysis
Matrix decomposition
Minimization
Noise
Noise reduction
Optimization
Restoration
Sparse matrices
Test procedures
weighted Schatten p-norm (WSN)
Wireless sensor networks
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2016.2547879