@article{babacan2008variational,
abstract = {In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters. {\textcopyright} 2008 IEEE.},
author = {Babacan, S. Derin and Molina, Rafael and Katsaggelos, Aggelos K.},
doi = {10.1109/TIP.2008.2007354},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Bayesian methods,Blind deconvolution,Parameter estimation,Total variation (TV),Variational methods},
number = {1},
pages = {12--26},
pmid = {19095515},
publisher = {IEEE},
title = {{Variational Bayesian blind deconvolution using a total variation prior}},
volume = {18},
year = {2009}
}

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