Image Denoising via L0 Gradient Minimization with Effective Fidelity Term

The sub(L0) gradient minimization (LGM) method has been proposed for image smoothing very recently. As an improvement of the total variation (TV) model which employs the sub(L1) norm of the gradient, the LGM model yields much better results for the piecewise constant image. However, just as the TV m...

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Published in:Mathematical problems in engineering 2015-01, Vol.2015
Main Authors: Zhang, Wenxue, Cao, Yongzhen, Zhang, Rongxin, Li, Lingling, Wen, Yunlei
Format: Article
Language:English
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Mathematical models
Minimization
Optimization
Production methods
Surface layer
Television
Texture
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Wen, Yunlei
description The sub(L0) gradient minimization (LGM) method has been proposed for image smoothing very recently. As an improvement of the total variation (TV) model which employs the sub(L1) 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.
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source Wiley Online Library Open Access; Publicly Available Content Database; IngentaConnect Journals
subjects Mathematical models
Minimization
Optimization
Production methods
Surface layer
Television
Texture
title Image Denoising via L0 Gradient Minimization with Effective Fidelity Term
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