Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model

Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model

Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and...

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Main Authors: Jianguang Zhu, Kai Li, Binbin Hao
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/6538610
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spelling doaj-e940dd9a20f74f538420b25a0aeea77f2020-11-25T01:42:57ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/65386106538610Image Restoration by a Mixed High-Order Total Variation and l1 Regularization ModelJianguang Zhu0Kai Li1Binbin Hao2College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaTotal variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and l1 regularization. The new model has separable structure which enables us to solve the involved subproblems more efficiently. We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM). Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.http://dx.doi.org/10.1155/2018/6538610
collection DOAJ
language English
format Article
sources DOAJ
author Jianguang Zhu
Kai Li
Binbin Hao
spellingShingle Jianguang Zhu
Kai Li
Binbin Hao
Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model
Mathematical Problems in Engineering
author_facet Jianguang Zhu
Kai Li
Binbin Hao
author_sort Jianguang Zhu
title Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model
title_short Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model
title_full Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model
title_fullStr Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model
title_full_unstemmed Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model
title_sort image restoration by a mixed high-order total variation and l1 regularization model
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and l1 regularization. The new model has separable structure which enables us to solve the involved subproblems more efficiently. We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM). Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.
url http://dx.doi.org/10.1155/2018/6538610
work_keys_str_mv AT jianguangzhu imagerestorationbyamixedhighordertotalvariationandl1regularizationmodel
AT kaili imagerestorationbyamixedhighordertotalvariationandl1regularizationmodel
AT binbinhao imagerestorationbyamixedhighordertotalvariationandl1regularizationmodel
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