Nonlocal Markovian models for image denoising

Author(s)

Salvadeo, Denis H. P. ^{[1, 2]} ; Mascarenhas, Nelson D. A. ^{[1, 3]} ; Levada, Alexandre L. M. ^[1]

Affiliation

^[1] Univ Fed Sao Carlos, Dept Comp, Via Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil

^[2] Sao Paulo State Univ, Dept Stat Appl Math & Computat, Rua 24A, 1515, BR-13503013 Rio Claro - Brazil

^[3] Fac Campo Limpo Paulista, Grad Program Comp Sci, Rua Guatemala 170, BR-13231230 Campo Limpo Paulista - Brazil

• Document type: Journal article
• Source: JOURNAL OF ELECTRONIC IMAGING; v. 25, n. 1 JAN 2016.
• Web of Science Citations: 6

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 (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 (AU)

• FAPESP's process: 10/09248-7 - Noise Filtering in Tomographic Images Acquired With Low Level Doses of Radiation Using a Contextual Bayesian Approach
• Grantee: Denis Henrique Pinheiro Salvadeo
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/25595-7 - Non local Markov Random Fields: a new model and its application in image denoising
• Grantee: Denis Henrique Pinheiro Salvadeo
• Support Opportunities: Regular Research Grants