@inproceedings{Babacan2007,
abstract = {In this paper we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. By following the hierarchical Bayesian framework, we simultaneously estimate the reconstructed image and the unknown hyperparameters for both the image prior and the image degradation noise. Our algorithms provide an approximation to the posterior distributions of the unknowns so that both the uncertainty of the estimates can be measured and different values from these distributions can be used for the estimates. We also show that some of the current approaches to TV-based image restoration are special cases of our variational framework. Experimental results show that the proposed approaches provide competitive performance witiiout any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included. {\textcopyright} 2007 IEEE.},
author = {Babacan, S. Derin and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2007 IEEE International Conference on Image Processing},
doi = {10.1109/ICIP.2007.4378900},
isbn = {978-1-4244-1436-9},
issn = {1522-4880},
keywords = {Bayesian methods,Image restoration,Parameter estimation,Total variation,Variational methods},
month = {sep},
pages = {I -- 97--I -- 100},
publisher = {IEEE},
title = {{Total Variation Image Restoration and Parameter Estimation using Variational Posterior Distribution Approximation}},
url = {http://ieeexplore.ieee.org/document/4378900/},
volume = {1},
year = {2007}
}

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We also show that some of the current approaches to TV-based image restoration are special cases of our variational framework. Experimental results show that the proposed approaches provide competitive performance witiiout any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included. © 2007 IEEE.","author":[{"propositions":[],"lastnames":["Babacan"],"firstnames":["S.","Derin"],"suffixes":[]},{"propositions":[],"lastnames":["Molina"],"firstnames":["Rafael"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]}],"booktitle":"2007 IEEE International Conference on Image Processing","doi":"10.1109/ICIP.2007.4378900","isbn":"978-1-4244-1436-9","issn":"1522-4880","keywords":"Bayesian methods,Image restoration,Parameter estimation,Total variation,Variational methods","month":"sep","pages":"I – 97–I – 100","publisher":"IEEE","title":"Total Variation Image Restoration and Parameter Estimation using Variational Posterior Distribution Approximation","url":"http://ieeexplore.ieee.org/document/4378900/","volume":"1","year":"2007","bibtex":"@inproceedings{Babacan2007,\nabstract = {In this paper we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. By following the hierarchical Bayesian framework, we simultaneously estimate the reconstructed image and the unknown hyperparameters for both the image prior and the image degradation noise. Our algorithms provide an approximation to the posterior distributions of the unknowns so that both the uncertainty of the estimates can be measured and different values from these distributions can be used for the estimates. We also show that some of the current approaches to TV-based image restoration are special cases of our variational framework. Experimental results show that the proposed approaches provide competitive performance witiiout any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included. {\\textcopyright} 2007 IEEE.},\nauthor = {Babacan, S. 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