Bayesian image restoration using a large-scale total patch variation prior
type: Item
instance of: scholarly article
title: Bayesian image restoration using a large-scale total patch variation prior
zbMATH Open document ID: 1235.94018
DOI: 10.1155/2011/408241
author: Weimin Yu
author: Yinsheng Li
author: Zhou Yang
author: Limin Luo
author: Yang Chen
author: Wufan Chen
published in: Mathematical Problems in Engineering
publication date: 2012-04-03T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 65D18
zbMATH DE Number: 6021084
Wikidata QID: Q58693016
describes a project that uses: CUDA
describes a project that uses: ForWaRD
describes a project that uses: TwIST
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2011/408241
OpenAlex ID: W2030843028
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description: scientific article; zbMATH DE number 6021084