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 |
Related resource: | http://dx.doi.org/10.1051/aas:1998259 |
ISSN: | 0365-0138 |
Appears in Collections: | Articles publicats en revistes (Física Quàntica i Astrofísica) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
500848.pdf | 491.38 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.