@inproceedings{babacan2008generalized,
abstract = {In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field (GGMRF) prior utilizing variational distribution approximation. The restored image and the unknown hyperparameters for both the image prior and the image degradation noise are simultaneously estimated within a hierarchical Bayesian framework. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Experimental results are provided to demonstrate the performance of the algorithms. {\textcopyright}2008 IEEE.},
author = {{Derin Babacan}, S. and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2008 IEEE International Conference on Acoustics, Speech and Signal Processing},
doi = {10.1109/ICASSP.2008.4517847},
isbn = {978-1-4244-1483-3},
issn = {1520-6149},
keywords = {Bayesian methods,Generalized gaussian Markov random fields,Image restoration,Parameter estimation,Variational methods},
month = {mar},
organization = {IEEE},
pages = {1265--1268},
publisher = {IEEE},
title = {{Generalized Gaussian Markov random field image restoration using variational distribution approximation}},
url = {http://ieeexplore.ieee.org/document/4517847/},
year = {2008}
}

{"_id":"vY9Fv2NjGijAKbgMT","bibbaseid":"derinbabacan-molina-katsaggelos-generalizedgaussianmarkovrandomfieldimagerestorationusingvariationaldistributionapproximation-2008","author_short":["Derin Babacan, S.","Molina, R.","Katsaggelos, A. K."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","abstract":"In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field (GGMRF) prior utilizing variational distribution approximation. The restored image and the unknown hyperparameters for both the image prior and the image degradation noise are simultaneously estimated within a hierarchical Bayesian framework. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Experimental results are provided to demonstrate the performance of the algorithms. ©2008 IEEE.","author":[{"propositions":[],"lastnames":["Derin Babacan"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Molina"],"firstnames":["Rafael"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K."],"suffixes":[]}],"booktitle":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","doi":"10.1109/ICASSP.2008.4517847","isbn":"978-1-4244-1483-3","issn":"1520-6149","keywords":"Bayesian methods,Generalized gaussian Markov random fields,Image restoration,Parameter estimation,Variational methods","month":"mar","organization":"IEEE","pages":"1265–1268","publisher":"IEEE","title":"Generalized Gaussian Markov random field image restoration using variational distribution approximation","url":"http://ieeexplore.ieee.org/document/4517847/","year":"2008","bibtex":"@inproceedings{babacan2008generalized,\nabstract = {In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field (GGMRF) prior utilizing variational distribution approximation. The restored image and the unknown hyperparameters for both the image prior and the image degradation noise are simultaneously estimated within a hierarchical Bayesian framework. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Experimental results are provided to demonstrate the performance of the algorithms. {\\textcopyright}2008 IEEE.},\nauthor = {{Derin Babacan}, S. and Molina, Rafael and Katsaggelos, Aggelos K.},\nbooktitle = {2008 IEEE International Conference on Acoustics, Speech and Signal Processing},\ndoi = {10.1109/ICASSP.2008.4517847},\nisbn = {978-1-4244-1483-3},\nissn = {1520-6149},\nkeywords = {Bayesian methods,Generalized gaussian Markov random fields,Image restoration,Parameter estimation,Variational methods},\nmonth = {mar},\norganization = {IEEE},\npages = {1265--1268},\npublisher = {IEEE},\ntitle = {{Generalized Gaussian Markov random field image restoration using variational distribution approximation}},\nurl = {http://ieeexplore.ieee.org/document/4517847/},\nyear = {2008}\n}\n","author_short":["Derin Babacan, S.","Molina, R.","Katsaggelos, A. K."],"key":"babacan2008generalized","id":"babacan2008generalized","bibbaseid":"derinbabacan-molina-katsaggelos-generalizedgaussianmarkovrandomfieldimagerestorationusingvariationaldistributionapproximation-2008","role":"author","urls":{"Paper":"http://ieeexplore.ieee.org/document/4517847/"},"keyword":["Bayesian methods","Generalized gaussian Markov random fields","Image restoration","Parameter estimation","Variational methods"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":["bayesian methods","generalized gaussian markov random fields","image restoration","parameter estimation","variational methods"],"search_terms":["generalized","gaussian","markov","random","field","image","restoration","using","variational","distribution","approximation","derin babacan","molina","katsaggelos"],"title":"Generalized Gaussian Markov random field image restoration using variational distribution approximation","year":2008}