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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/8546
Image restoration in astronomy: a Bayesian perspective
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When preparing an article on image restoration in astronomy, it is obvious that some topics have to be dropped to keep the work at reasonable length. We have decided to concentrate on image and noise models and on the algorithms to find the restoration. Topics like parameter estimation and stopping rules are also commented on. We start by describing the Bayesian paradigm and then proceed to study the noise and blur models used by the astronomical community. Then the prior models used to restore astronomical images are examined. We describe the algorithms used to find the restoration for the most common combinations of degradation and image models. Then we comment on important issues such as acceleration of algorithms, stopping rules, and parameter estimation. We also comment on the huge amount of information available to, and made available by, the astronomical community.
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MOLINA, Rafael (Molina Porto), et al. Image restoration in astronomy: a Bayesian perspective. IEEE Signal Processing Magazine. 2001. Vol. 18, num. 2, pags. 11-29. ISSN 1053-5888. [consulted: 17 of June of 2026]. Available at: https://hdl.handle.net/2445/8546