# name
Bayesian image reconstruction with space-variant noise suppression

# metadata.dc.description.abstract[].value
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.

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Astronomy and Astrophysics Supplement Series, 1998, vol. 131, núm. 2, p. 167-180.

# metadata.dc.rights[].value
(c) The European Southern Observatory, 1998

# metadata.dc.source[].value
Articles publicats en revistes (Física Quàntica i Astrofísica)

# metadata.dc.title[].value
Bayesian image reconstruction with space-variant noise suppression