@inproceedings{babacan2008total,
abstract = {In this paper we propose a novel algorithm for super resolution based on total variation prior and variational distribution approximations. We formulate the problem using a hierarchical Bayesian model where the reconstructed high resolution image and the model parameters are estimated simultaneously from the low resolution observations. The algorithm resulting from this formulation utilizes variational inference and provides approximations to the posterior distributions of the latent variables. Due to the simultaneous parameter estimation, the algorithm is fully automated so parameter tuning is not required. Experimental results show that the proposed approach outperforms some of the state-of-the-art super resolution algorithms. {\textcopyright} 2008 IEEE.},
author = {Babacan, S. Derin and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2008 15th IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2008.4711836},
isbn = {978-1-4244-1765-0},
issn = {15224880},
keywords = {Bayesian methods,Parameter estimation,Super resolution,Total variation,Variational methods},
organization = {IEEE},
pages = {641--644},
publisher = {IEEE},
title = {{Total variation super resolution using a variational approach}},
url = {http://ieeexplore.ieee.org/document/4711836/},
year = {2008}
}

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