A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise

This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome...

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Autores principales:	Zhijun Luo, Zhibin Zhu, Benxin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/3bcd11db09474d5dac2c13c35b484e6e
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id	oai:doaj.org-article:3bcd11db09474d5dac2c13c35b484e6e
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spelling	oai:doaj.org-article:3bcd11db09474d5dac2c13c35b484e6e2021-11-08T02:37:07ZA LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise1607-887X10.1155/2021/3289477https://doaj.org/article/3bcd11db09474d5dac2c13c35b484e6e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3289477https://doaj.org/toc/1607-887XThis paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models.Zhijun LuoZhibin ZhuBenxin ZhangHindawi LimitedarticleMathematicsQA1-939ENDiscrete Dynamics in Nature and Society, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Zhijun Luo Zhibin Zhu Benxin Zhang A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise
description	This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models.
format	article
author	Zhijun Luo Zhibin Zhu Benxin Zhang
author_facet	Zhijun Luo Zhibin Zhu Benxin Zhang
author_sort	Zhijun Luo
title	A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise
title_short	A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise
title_full	A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise
title_fullStr	A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise
title_full_unstemmed	A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise
title_sort	logtvscad nonconvex regularization model for image deblurring in the presence of impulse noise
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/3bcd11db09474d5dac2c13c35b484e6e
work_keys_str_mv	AT zhijunluo alogtvscadnonconvexregularizationmodelforimagedeblurringinthepresenceofimpulsenoise AT zhibinzhu alogtvscadnonconvexregularizationmodelforimagedeblurringinthepresenceofimpulsenoise AT benxinzhang alogtvscadnonconvexregularizationmodelforimagedeblurringinthepresenceofimpulsenoise AT zhijunluo logtvscadnonconvexregularizationmodelforimagedeblurringinthepresenceofimpulsenoise AT zhibinzhu logtvscadnonconvexregularizationmodelforimagedeblurringinthepresenceofimpulsenoise AT benxinzhang logtvscadnonconvexregularizationmodelforimagedeblurringinthepresenceofimpulsenoise
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A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise

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