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Journal of Electronic Imaging

Nonlocal Markovian models for image denoising
Author(s): Denis H. P. Salvadeo; Nelson D. A. Mascarenhas; Alexandre L. M. Levada
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Paper 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.

Paper Details

Date Published: 7 January 2016
PDF: 20 pages
J. Electron. Imag. 25(1) 013003 doi: 10.1117/1.JEI.25.1.013003
Published in: Journal of Electronic Imaging Volume 25, Issue 1
Show Author Affiliations
Denis H. P. Salvadeo, UNESP (Brazil)
Univ. Federal de São Carlos (Brazil)
Nelson D. A. Mascarenhas, Univ. Federal de São Carlos (Brazil)
FACCAMP (Brazil)
Alexandre L. M. Levada, Univ. Federal de São Carlos (Brazil)

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