Issue |
Astron. Astrophys. Suppl. Ser.
Volume 131, Number 1, July 1998
|
|
---|---|---|
Page(s) | 167 - 180 | |
DOI | https://doi.org/10.1051/aas:1998259 | |
Published online | 15 July 1998 |
Bayesian image reconstruction with space-variant noise suppression
1
Departament d'Astronomia i Meteorologia, Universitat de Barcelona, Av. Diagonal 647, E-08028 Barcelona and Observatorio Fabra, Barcelona, Spain
2
Engineering Division, Lawrence Berkeley Laboratory, University of California, Berkeley, CA 94720, U.S.A.
Send offprint request to: J. Núñez
Received:
26
March
1997
Accepted:
5
February
1998
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.
Key words: techniques: image processing / methods: data analysis
© European Southern Observatory (ESO), 1998