Image restoration by a mixed high-order total variation and \(l_1\) regularization model
type: Item
instance of: scholarly article
title: Image restoration by a mixed high-order total variation and \(l_1\) regularization model
zbMATH Open document ID: 1427.94036
DOI: 10.1155/2018/6538610
author: Jianguang Zhu
author: Kai Li
author: Binbin Hao
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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 \(l_1\) 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.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 65R32
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 7019327
Wikidata QID: Q113078553
describes a project that uses: RecPF
describes a project that uses: TwIST
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/6538610
OpenAlex ID: W2890431627
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description: scientific article; zbMATH DE number 7019327