Persistent Link:
http://hdl.handle.net/10150/284110
Title:
Multiframe restoration methods for image synthesis and recovery
Author:
Green, Joseph Jacob
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 multiple-frame (multiframe) reconstructions using imagery acquired in dynamic imaging environments. Through several interesting examples, we address and relate the key concepts of information weighting, channel diversity and multiframe processing in the context of producing high resolution estimates from severely degraded imagery. For the problem of space object identification, we look at methods for preprocessing a collection of atmospheric turbulence-degraded short-exposure images to improve the resolving power of estimation algorithms. Specifically, we examine the performance of using frame selection to extract the least degraded subset of images from an ensemble for processing. Several measures of image quality are compared against idealized standards to demonstrate their relative effectiveness for ranking highly the least degraded image frames. We also examine the resolving implication of removing additive background noise, resulting from the sky and telescope. Specifically, we show that background compensation acts as a defacto restoration of the compact object support and leads to furthering the resolving power of estimation algorithms. In the context of dilute aperture imagery, we look at methods for inducing channel diversity into a collection of measurements. With a diverse image set, we compute estimates using both a joint multiframe objective and an aggregated objective. We then examine the implication of using joint or aggregate objectives in any estimation algorithm from a set-theoretic standpoint. Finally, we extend the classic Wiener filter for the multiframe case. The resulting formulation demonstrates that the appropriate weighting of image data allows for the worst frames to be included while improving the restoration. We discuss how this contradicts the earlier idea of frame selection and relates the multiframe Wiener filter to the dual information theoretic concept of "water-filling".
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Physics, Optics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Hunt, Bobby R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleMultiframe restoration methods for image synthesis and recoveryen_US
dc.creatorGreen, Joseph Jacoben_US
dc.contributor.authorGreen, Joseph Jacoben_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 multiple-frame (multiframe) reconstructions using imagery acquired in dynamic imaging environments. Through several interesting examples, we address and relate the key concepts of information weighting, channel diversity and multiframe processing in the context of producing high resolution estimates from severely degraded imagery. For the problem of space object identification, we look at methods for preprocessing a collection of atmospheric turbulence-degraded short-exposure images to improve the resolving power of estimation algorithms. Specifically, we examine the performance of using frame selection to extract the least degraded subset of images from an ensemble for processing. Several measures of image quality are compared against idealized standards to demonstrate their relative effectiveness for ranking highly the least degraded image frames. We also examine the resolving implication of removing additive background noise, resulting from the sky and telescope. Specifically, we show that background compensation acts as a defacto restoration of the compact object support and leads to furthering the resolving power of estimation algorithms. In the context of dilute aperture imagery, we look at methods for inducing channel diversity into a collection of measurements. With a diverse image set, we compute estimates using both a joint multiframe objective and an aggregated objective. We then examine the implication of using joint or aggregate objectives in any estimation algorithm from a set-theoretic standpoint. Finally, we extend the classic Wiener filter for the multiframe case. The resulting formulation demonstrates that the appropriate weighting of image data allows for the worst frames to be included while improving the restoration. We discuss how this contradicts the earlier idea of frame selection and relates the multiframe Wiener filter to the dual information theoretic concept of "water-filling".en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectPhysics, Optics.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHunt, Bobby R.en_US
dc.identifier.proquest9965898en_US
dc.identifier.bibrecord.b40482017en_US
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