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Bayesian Image Restoration Using A Large-scale Total Patch Variation Prior

Yang Chen, Weimin Yu, Yinsheng Li, Zhou Yang, Limin Luo, Wufan Chen
LTSI – Laboratoire Traitement du Signal et de l’Image
Mathematical Problems in Engineering 2011, Article ID 408241, 2011

@article{yang2011bayesian,

   title={Bayesian Image Restoration Using a Large-Scale Total Patch Variation Prior},

   author={Yang, C. and Weimin, Y. and Yinsheng, L. and Zhou, Y. and Limin, L. and Wufan, C.},

   journal={Mathematical Problems in Engineering},

   volume={2011},

   year={2011},

   publisher={Hindawi Publishing Corporation}

}

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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.
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