This dissertation is concerned with an image processing algorithm that performs image enhancement and restoration. Closed form maximum entropy filtering will be derived from its foundations in classical Wiener filtering and maximum entropy theory. Ad hoc variations of Wiener filtering will be introduced and discussed in terms of information density. The language of information density will be used to examine the entropy filter and its merits. These merits will be demonstrated through a series of numerical simulations of real and artificial astronomical objects. The results of these simulations will be shown to be a 7% to 50% improvement over the classical Wiener estimate. The closed form maximum entropy filter will be adapted to the blind deconvolution problem. A test pattern will be estimated to demonstrate the potential power of this adaptation.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/289224 |
| Date | January 2000 |
| Creators | Graser, David Jay |
| Contributors | Frieden, B. Roy |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Dissertation-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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