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Proceedings Paper

Bayesian image reconstruction with variable balancing parameter
Author(s): Jorge Nunez; Jorge Llacer
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Paper Abstract

This paper describes the Bayesian image reconstruction algorithm with entropy prior with space variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of high and low signal/noise ratio, thus avoiding the large residuals present in algorithms that use a constant balancing parameter. The space variant hyperparameter determines the relative weight between the prior information and the likelihood defining the degree of smoothness of the solution. To compute the variable hyperparameter we used a segmentation technique based on artificial neural networks of the Self- Organizing Map type. Using this technique we segmented the image in 25 regions and computed a different value of the hyperparameter for each one. We applied the method to the Hubble Space Telescope Cameras and to ground based CCD data.

Paper Details

Date Published: 1 June 1994
PDF: 11 pages
Proc. SPIE 2198, Instrumentation in Astronomy VIII, (1 June 1994); doi: 10.1117/12.176821
Show Author Affiliations
Jorge Nunez, Univ. de Barcelona (Spain)
Jorge Llacer, Lawrence Berkeley Lab. (United States)

Published in SPIE Proceedings Vol. 2198:
Instrumentation in Astronomy VIII
David L. Crawford; Eric R. Craine, Editor(s)

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