Hybrid Model and Split Bregman Iteration Algorithm for Image Denoising

DOI

10.2991/ifmeita-17.2018.61How to use a DOI?

Keywords

hybrid model; difference of convex algorithm; split Bregman iteration; image denoising.

Abstract

Although difference of convex model has attracted many research efforts due to its superior performance for image processing, no attention has focused on robust data fidelity for this model. In this paper, we propose a novel model, which combines the and fidelity terms with a weighted difference of anisotropic and isotropic total variation (TV). Since our model takes a new difference form of convex terms, we employ difference of convex algorithm (DCA). In this paper, we adopt split Bregman iteration (SBI) to solve each DCA subproblem of the proposed model. Image denoising verifies the convergence of optimal solution and monotone decreasing of objective function. Experimental results on image denoising demonstrate that the proposed methods outperform other competing methods in terms of quantitative criteria and perceptual quality.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yibin Yu
AU  - Jialin Zhang
PY  - 2018/02
DA  - 2018/02
TI  - Hybrid Model and Split Bregman Iteration Algorithm for Image Denoising
BT  - Proceedings of the 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017)
PB  - Atlantis Press
SP  - 361
EP  - 368
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-17.2018.61
DO  - 10.2991/ifmeita-17.2018.61
ID  - Yu2018/02
ER  -