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500848.pdf (491.38 KB)

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Data de publicació

1998

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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/9222

Bayesian image reconstruction with space-variant noise suppression

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Recurs relacionat

http://dx.doi.org/10.1051/aas:1998259

Resum

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.

Matèries

Processament de dades, Anàlisi de dades, Estadística bayesiana

Matèries (anglès)

Image processing, Data analysis

Citació

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Articles publicats en revistes (Física Quàntica i Astrofísica)

Citació

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: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/9222

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