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Mathematical Problems in Engineering
Volume 2011, Article ID 408241, 15 pages
Research Article

Bayesian Image Restoration Using a Large-Scale Total Patch Variation Prior

1Laboratory of Image Science and Technology, Southeast University, 211514 Nanjing, China
2Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France
3Laboratoire Traitement du Signal et de lImage (LTSI) INSERM U642, Universite de Rennes I, Campus de Beaulieu, 263 avenue du General Leclerc, CS 74205, 35042 Rennes Cedex, France
4School of Biomedical Engineering, Southern Medical University, 510515 Guangzhou, China

Received 5 September 2010; Revised 30 November 2010; Accepted 27 April 2011

Academic Editor: Nickolas S. Sapidis

Copyright © 2011 Yang Chen 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.


Edge-preserving Bayesian restorations using nonquadratic priors are often inefficient in restoring continuous variations and tend to produce block artifacts around edges in ill-posed inverse image restorations. To overcome this, we have proposed a spatial adaptive (SA) prior with improved performance. However, this SA prior restoration suffers from high computational cost and the unguaranteed convergence problem. Concerning these issues, this paper proposes a Large-scale Total Patch Variation (LS-TPV) Prior model for Bayesian image restoration. In this model, the prior for each pixel is defined as a singleton conditional probability, which is in a mixture prior form of one patch similarity prior and one weight entropy prior. A joint MAP estimation is thus built to ensure the iteration monotonicity. The intensive calculation of patch distances is greatly alleviated by the parallelization of Compute Unified Device Architecture(CUDA). Experiments with both simulated and real data validate the good performance of the proposed restoration.