Núñez de Murga, Jorge, 1955-Llacer, Jorge2009-08-282009-08-2819980365-0138https://hdl.handle.net/2445/9222In 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.14 p.application/pdfeng(c) The European Southern Observatory, 1998Processament de dadesAnàlisi de dadesEstadística bayesianaImage processingData analysisinfo:eu-repo/semantics/article500848info:eu-repo/semantics/openAccess