@incollection{Antonio2003a,
abstract = {In this work we propose a now method to estimate the scale hyperparameter for convex priors with scalable energy functions in Single Photon Emission Computed Tomography (SPECT) image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. The proposed method is tested on synthetic SPECT images using Generalized Gaussian Markov Random Fields (GGMRF) as scalable prior distributions. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
author = {L{\'{o}}pez, Antonio 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 = {10.1007/978-3-540-44871-6_52},
isbn = {3540402179},
issn = {16113349},
pages = {445--452},
title = {{Bayesian SPECT Image Reconstruction with Scale Hyperparameter Estimation for Scalable Prior}},
url = {http://link.springer.com/10.1007/978-3-540-44871-6_52},
volume = {2652},
year = {2003}
}

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