A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise
Joint Authors
Pan, Han
Qiao, Lingfeng
Li, Minzhe
Jing, Zhongliang
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing.
However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component.
In this paper, a new problem formulation with regular spectral k-support norm and regular k-support l1 norm is proposed.
A unified framework is developed to capture the intrinsic sparsity structure of all two components.
To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed.
This scheme is achieved by alternating regular k-shrinkage thresholding operator.
Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method.
American Psychological Association (APA)
Pan, Han& Jing, Zhongliang& Qiao, Lingfeng& Li, Minzhe. 2017. A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise. Applied Computational Intelligence and Soft Computing،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1121416
Modern Language Association (MLA)
Pan, Han…[et al.]. A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise. Applied Computational Intelligence and Soft Computing No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1121416
American Medical Association (AMA)
Pan, Han& Jing, Zhongliang& Qiao, Lingfeng& Li, Minzhe. A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise. Applied Computational Intelligence and Soft Computing. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1121416
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121416