Electronic Resource
Springer
Astrophysics and space science
171 (1990), S. 341-349
ISSN:
1572-946X
Source:
Springer Online Journal Archives 1860-2000
Topics:
Physics
Notes:
Abstract This paper presents the development and testing of a new iterative reconstruction algorithm for astronomy. We propose a maximuma posteriori method of image reconstruction in the Bayesian statistical framework for the Poisson noise case. The method uses the entropy with an adjustable ‘sharpness parameter’ to define the prior probability and the likelihood with ‘data increment’ parameters to define the conditional probability. The method allows us to obtain reconstructions with neither the problem of the ‘grey’ reconstructions associated with the pure Bayesian reconstructions nor the problem of image deterioration, typical of the maximum likelihood method. Our iterative algorithm is fast, stable, maintains positivity, and converges to feasible images.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00646875
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