@inproceedings{babacan2012bayesian,
abstract = {We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible. {\textcopyright} 2012 Springer-Verlag.},
author = {Babacan, S. Derin and Molina, Rafael and Do, Minh N. and Katsaggelos, Aggelos K.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-33783-3_25},
isbn = {9783642337826},
issn = {03029743},
number = {PART 6},
organization = {Springer Berlin Heidelberg},
pages = {341--355},
title = {{Bayesian Blind Deconvolution with General Sparse Image Priors}},
url = {http://link.springer.com/10.1007/978-3-642-33783-3_25},
volume = {7577 LNCS},
year = {2012}
}

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