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Nonlocal Markovian models for image denoising

[+] Author Affiliations
Denis H. P. Salvadeo

São Paulo State University, Department of Statistics, Applied Mathematics and Computation, Rua 24A, 1515, Rio Claro 13503-013, Brazil

Federal University of São Carlos, Computing Department, Via Washington Luis, Km 235, São Carlos 13565-905, Brazil

Nelson D. A. Mascarenhas

Federal University of São Carlos, Computing Department, Via Washington Luis, Km 235, São Carlos 13565-905, Brazil

Faculdade Campo Limpo Paulista, Graduate Program in Computer Science, Rua Guatemala, 170, Campo Limpo Paulista 13231-230, Brazil

Alexandre L. M. Levada

Federal University of São Carlos, Computing Department, Via Washington Luis, Km 235, São Carlos 13565-905, Brazil

J. Electron. Imaging. 25(1), 013003 (Jan 07, 2016). doi:10.1117/1.JEI.25.1.013003
History: Received June 17, 2015; Accepted December 8, 2015
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Abstract.  Currently, the state-of-the art methods for image denoising are patch-based approaches. Redundant information present in nonlocal regions (patches) of the image is considered for better image modeling, resulting in an improved quality of filtering. In this respect, nonlocal Markov random field (MRF) models are proposed by redefining the energy functions of classical MRF models to adopt a nonlocal approach. With the new energy functions, the pairwise pixel interaction is weighted according to the similarities between the patches corresponding to each pair. Also, a maximum pseudolikelihood estimation of the spatial dependency parameter (β) for these models is presented here. For evaluating this proposal, these models are used as an a priori model in a maximum a posteriori estimation to denoise additive white Gaussian noise in images. Finally, results display a notable improvement in both quantitative and qualitative terms in comparison with the local MRFs.

© 2016 SPIE and IS&T

Citation

Denis H. P. Salvadeo ; Nelson D. A. Mascarenhas and Alexandre L. M. Levada
"Nonlocal Markovian models for image denoising", J. Electron. Imaging. 25(1), 013003 (Jan 07, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.1.013003


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