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Abstract and Applied Analysis
Volume 2013, Article ID 213536, 11 pages
Research Article

An Efficient Variational Method for Image Restoration

1School of Mathematical Sciences/Institute of Computational Science, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
2School of Science, Huaihai Institute of Technology, Lianyungang, Jiangsu 222005, China

Received 29 July 2013; Accepted 14 October 2013

Academic Editor: Peilin Shi

Copyright © 2013 Jun Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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).