@inproceedings{dc9a9c10e6eb41669625203ad2b68205,
title = "Parameter estimation in Bayesian Blind Deconvolution with super Gaussian image priors",
abstract = "Super Gaussian (SG) distributions have proven to be very powerful prior models to induce sparsity in Bayesian Blind Deconvolution (BD) problems. Their conjugate based representations make them specially attractive when Variational Bayes (VB) inference is used since their variational parameters can be calculated in closed form with the sole knowledge of the energy function of the prior model. In this work we show how the introduction in the SG distribution of a global strength (not necessary scale) parameter can be used to improve the quality of the obtained restorations as well as to introduce additional information on the global weight of the prior. A model to estimate the new unknown parameter within the Bayesian framework is provided. Experimental results, on both synthetic and real images, demonstrate the effectiveness of the proposed approach.",
keywords = "Bayesian methods, Super Gaussian, blind deconvolution, image processing, image restoration",
author = "Miguel Vega and Rafael Molina and Katsaggelos, {Aggelos K.}",
note = "Publisher Copyright: {\textcopyright} 2014 EURASIP.; 22nd European Signal Processing Conference, EUSIPCO 2014 ; Conference date: 01-09-2014 Through 05-09-2014",
year = "2014",
month = nov,
day = "10",
language = "English (US)",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
pages = "1632--1636",
booktitle = "2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014",
}