An efficient variational method for image restoration
type: Item
instance of: scholarly article
title: An efficient variational method for image restoration
zbMATH Open document ID: 1319.94017
DOI: 10.1155/2013/213536
author: Jun Liu
author: Ting-Zhu Huang
author: Xiao-Guang Lv
author: Si Wang
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle's algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM).
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 6306536
zbMATH Keywords: constrained minimization problem
zbMATH Keywords: double total variation regularization terms
zbMATH Keywords: split Bregman iteration method
zbMATH Keywords: Chambolle's algorithm
zbMATH Keywords: convergence
Wikidata QID: Q58915572
describes a project that uses: RecPF
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/213536
OpenAlex ID: W1967358198
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description: scientific article; zbMATH DE number 6306536