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Título:	Bayesian image reconstruction with space-variant noise suppression
Autor/a:	Núñez de Murga, Jorge, 1955-; Llacer, Jorge
Otros autores:	Universitat de Barcelona
Abstract:	In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.
Materia(s):	-Processament de dades -Anàlisi de dades -Estadística bayesiana -Image processing -Data analysis
Derechos:	(c) The European Southern Observatory, 1998
Tipo de documento:	Artículo Artículo - Versión publicada
Editor:	EDP Sciences
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