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Bayesian image reconstruction with space-variant noise suppression Núñez de Murga, Jorge, 1955- Llacer, Jorge Processament de dades Anàlisi de dades Estadística bayesiana Image processing Data analysis 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. 2009-08-28T08:23:48Z 2009-08-28T08:23:48Z 1998 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 0365-0138 https://hdl.handle.net/2445/9222 500848 eng Reproducció del document publicat a http://dx.doi.org/10.1051/aas:1998259 Astronomy and Astrophysics Supplement Series, 1998, vol. 131, núm. 2, p. 167-180. http://dx.doi.org/10.1051/aas:1998259 (c) The European Southern Observatory, 1998 info:eu-repo/semantics/openAccess 14 p. application/pdf EDP Sciences Articles publicats en revistes (Física Quàntica i Astrofísica)