Carregant...
Fitxers
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Tots els drets reservats
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
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
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 (anglès)
Citació
Citació
NÚÑEZ DE MURGA, Jorge, LLACER, Jorge. Bayesian image reconstruction with space-variant noise suppression. _Astronomy and Astrophysics Supplement Series_. 1998. Vol. 131, núm. 2, pàgs. 167-180. [consulta: 26 de desembre de 2025]. ISSN: 0365-0138. [Disponible a: https://hdl.handle.net/2445/9222]