@inproceedings{bc99c7120162433e8072a14e093afeed,
title = "From global to local Bayesian parameter estimation in image restoration using variational distribution approximations",
abstract = "In this paper we present a new Bayesian methodology for the restoration of blurred and noisy images. Bayesian methods rely on image priors that encapsulate prior image knowledge and avoid the ill-posedness of the image restoration problems. Some of these priors depend on global variance parameters, unable to account for local characteristics. Here we first use variational methods to approximate probability posterior distributions for the global model to later use those distributions to define local and more realistic image models which lead to better restored images as it is shown in the experimental section.",
keywords = "Bayesian models, Image restoration, Parameter estimation, Regularization, Variational methods",
author = "Rafael Molina and Miguel Vega and Katsaggelos, {Aggelos K}",
year = "2006",
month = dec,
day = "1",
doi = "10.1109/ICIP.2007.4378906",
language = "English (US)",
isbn = "1424414377",
series = "Proceedings - International Conference on Image Processing, ICIP",
booktitle = "2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings",
note = "14th IEEE International Conference on Image Processing, ICIP 2007 ; Conference date: 16-09-2007 Through 19-09-2007",
}