Image denoising via \(L_0\) gradient minimization with effective fidelity term
type: Item
instance of: scholarly article
title: Image denoising via \(L_0\) gradient minimization with effective fidelity term
zbMATH Open document ID: 1395.94074
DOI: 10.1155/2015/712801
author: Yongzhen Cao
author: Rongxin Zhang
author: Yunlei Wen
author: Wen-Xue Zhang
author: Lingling Li
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: The \(L_0\) gradient minimization (LGM) method has been proposed for image smoothing very recently. As an improvement of the total variation (TV) model which employs the \(L_1\) norm of the gradient, the LGM model yields much better results for the piecewise constant image. However, just as the TV model, the LGM model also suffers, even more seriously, from the staircasing effect and the inefficiency in preserving the texture in image. In order to overcome these drawbacks, in this paper, we propose to introduce an effective fidelity term into the LGM model. The fidelity term is an exemplar of the moving least square method using steering kernel. Under this framework, these two methods benefit from each other and can produce better results. Experimental results show that the proposed scheme is promising as compared with the state-of-the-art methods.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 6927053
describes a project that uses: RecPF
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/712801
OpenAlex ID: W2201327521
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description: scientific article; zbMATH DE number 6927053