Nonlocal Markovian models for image denoising
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Data
2016-01-01
Autores
Salvadeo, Denis H. P. [UNESP]
Mascarenhas, Nelson D. A.
Levada, Alexandre L. M.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Is&t & Spie
Resumo
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 (beta) 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. (C) 2016 SPIE and IS&T
Descrição
Palavras-chave
image denoising, maximum pseudolikelihood estimation, Markov random fields, nonlocal patch-based approach, parameter estimation
Como citar
Journal Of Electronic Imaging. Bellingham: Is&t & Spie, v. 25, n. 1, 20 p., 2016.