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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/9222
Bayesian image reconstruction with space-variant noise suppression
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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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NÚÑEZ DE MURGA, Jorge and LLACER, Jorge. Bayesian image reconstruction with space-variant noise suppression. Astronomy and Astrophysics Supplement Series. 1998. Vol. 131, num. 2, pags. 167-180. ISSN 0365-0138. [consulted: 13 of June of 2026]. Available at: https://hdl.handle.net/2445/9222