@incollection{Salvador2009,
abstract = {This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, utilizing the variational approximation within the Bayesian paradigm. The proposed inference procedure requires the calculation of the covariance matrix of the HR image given the LR observations and the unknown hyperparameters of the probabilistic model. Unfortunately the size and complexity of such matrix renders its calculation impossible, and we propose and compare three alternative approximations. The estimated HR images are compared with images provided by other HR reconstruction methods. {\textcopyright} 2009 Springer Berlin Heidelberg.},
author = {Villena, Salvador and Vega, Miguel and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-04697-1_18},
isbn = {3642046967},
issn = {03029743},
keywords = {Bayesian paradigm,Covariance matrix calculation,High resolution images,Variational inference},
pages = {188--199},
title = {{Parameter Estimation in Bayesian Super-Resolution Image Reconstruction from Low Resolution Rotated and Translated Images}},
url = {http://link.springer.com/10.1007/978-3-642-04697-1_18},
volume = {5807 LNCS},
year = {2009}
}

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