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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/3289477 |
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| author | Zhijun Luo Zhibin Zhu Benxin Zhang |
| author_facet | Zhijun Luo Zhibin Zhu Benxin Zhang |
| author_sort | Zhijun Luo |
| collection | DOAJ |
| 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 |
| id | doaj-art-3bcd11db09474d5dac2c13c35b484e6e |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-3bcd11db09474d5dac2c13c35b484e6e2025-02-03T01:25:49ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/3289477A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse NoiseZhijun Luo0Zhibin Zhu1Benxin Zhang2School of Electronic Engineering and AutomationSchool of Mathematics and Computing ScienceSchool of Electronic Engineering and AutomationThis 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.http://dx.doi.org/10.1155/2021/3289477 |
| spellingShingle | Zhijun Luo Zhibin Zhu Benxin Zhang A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise Discrete Dynamics in Nature and Society |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2021/3289477 |
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