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Advances in Multimedia
Volume 2014, Article ID 934834, 10 pages
Research Article

An Adaptive Image Denoising Model Based on Tikhonov and TV Regularizations

1School of Computer and Information, Hefei University of Technology, Hefei 23009, China
2School of Computer and Information, Anqing Normal University, Anqing 246011, China

Received 23 January 2014; Revised 11 July 2014; Accepted 14 July 2014; Published 4 August 2014

Academic Editor: Jianping Fan

Copyright © 2014 Kui 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.


To avoid the staircase artifacts, an adaptive image denoising model is proposed by the weighted combination of Tikhonov regularization and total variation regularization. In our model, Tikhonov regularization and total variation regularization can be adaptively selected based on the gradient information of the image. When the pixels belong to the smooth regions, Tikhonov regularization is adopted, which can eliminate the staircase artifacts. When the pixels locate at the edges, total variation regularization is selected, which can preserve the edges. We employ the split Bregman method to solve our model. Experimental results demonstrate that our model can obtain better performance than those of other models.