Abstract
Formal Bayesian methods have only a little history in astronomical applications, yet they have recently become the favorite methodology for statisticians studying image analysis. The “prior distributions” used are spatial stochastic processes which aim to encapsulate the relevant features of the images which are known from past experience. We describe two recent applications of these methods by the author and his colleagues to deconvolution of CCD images blurred by atmospheric motion, and to automatically “sketching” spiral galaxies as a prelude to classification. The deconvolution compares favorably with Maximum Entropy in speed, fit to the data, lack of artifacts, and visual acceptability (at least to our astronomer colleagues). The sketching process produces consistent sketches from quite faint spirals.
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Ripley, B.D. (1992). Bayesian Methods of Deconvolution and Shape Classification. In: Feigelson, E.D., Babu, G.J. (eds) Statistical Challenges in Modern Astronomy. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9290-3_38
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DOI: https://doi.org/10.1007/978-1-4613-9290-3_38
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