Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 617026, 9 pages
Research Article

Fast Total-Variation Image Deconvolution with Adaptive Parameter Estimation via Split Bregman Method

1Unit 302, Xi’an Institute of High-tech, Xi’an 710025, China
2Unit 403, Xi’an Institute of High-tech, Xi’an 710025, China

Received 16 August 2013; Accepted 27 December 2013; Published 17 February 2014

Academic Editor: Yi-Hung Liu

Copyright © 2014 Chuan He 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.


The total-variation (TV) regularization has been widely used in image restoration domain, due to its attractive edge preservation ability. However, the estimation of the regularization parameter, which balances the TV regularization term and the data-fidelity term, is a difficult problem. In this paper, based on the classical split Bregman method, a new fast algorithm is derived to simultaneously estimate the regularization parameter and to restore the blurred image. In each iteration, the regularization parameter is refreshed conveniently in a closed form according to Morozov’s discrepancy principle. Numerical experiments in image deconvolution show that the proposed algorithm outperforms some state-of-the-art methods both in accuracy and in speed.