Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/9222
Title: Bayesian image reconstruction with space-variant noise suppression
Author: Núñez de Murga, Jorge, 1955-
Llacer, Jorge
Keywords: Processament de dades
Anàlisi de dades
Estadística bayesiana
Image processing
Data analysis
Issue Date: 1998
Publisher: EDP Sciences
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.
Note: Reproducció del document publicat a http://dx.doi.org/10.1051/aas:1998259
It is part of: Astronomy and Astrophysics Supplement Series, 1998, vol. 131, núm. 2, p. 167-180.
URI: http://hdl.handle.net/2445/9222
ISSN: 0365-0138
Appears in Collections:Articles publicats en revistes (Física Quàntica i Astrofísica)

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