Image restoration and enhancement by closed form positively constrained maximum entropy

Persistent Link:
http://hdl.handle.net/10150/289224
Title:
Image restoration and enhancement by closed form positively constrained maximum entropy
Author:
Graser, David Jay
Issue Date:
2000
Publisher:
The University of Arizona.
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.
Abstract:
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.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Physics, Optics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Optical Sciences
Degree Grantor:
University of Arizona
Advisor:
Frieden, B. Roy

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleImage restoration and enhancement by closed form positively constrained maximum entropyen_US
dc.creatorGraser, David Jayen_US
dc.contributor.authorGraser, David Jayen_US
dc.date.issued2000en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © 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.en_US
dc.description.abstractThis 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.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectPhysics, Optics.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorFrieden, B. Royen_US
dc.identifier.proquest9992112en_US
dc.identifier.bibrecord.b41170611en_US
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