Bayesian image reconstruction with variable balancing parameter

Jorge Nunez; Jorge Llacer

doi:10.1117/12.176821

1 June 1994 Bayesian image reconstruction with variable balancing parameter

Jorge Nunez, Jorge Llacer

Proceedings Volume 2198, Instrumentation in Astronomy VIII; (1994) https://doi.org/10.1117/12.176821
Event: 1994 Symposium on Astronomical Telescopes and Instrumentation for the 21st Century, 1994, Kailua, Kona, HI, United States

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.

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Jorge Nunez and Jorge Llacer "Bayesian image reconstruction with variable balancing parameter", Proc. SPIE 2198, Instrumentation in Astronomy VIII, (1 June 1994); https://doi.org/10.1117/12.176821

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KEYWORDS

Image segmentation

Reconstruction algorithms

Image processing algorithms and systems

Stars

Expectation maximization algorithms

Image restoration

Algorithm development

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