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dc.contributor.advisorHunt, Bobby R.en_US
dc.contributor.authorMiller, Casey Lee
dc.creatorMiller, Casey Leeen_US
dc.date.accessioned2013-05-09T09:21:01Z
dc.date.available2013-05-09T09:21:01Z
dc.date.issued1999en_US
dc.identifier.urihttp://hdl.handle.net/10150/288963
dc.description.abstractMethods for the restoration of images corrupted by blur and noise are presented. During transmission through an optical or electrical channel, images become corrupted by blur and noise as a result of channel limitations (i.e. optical aberrations or a bandlimit). If images are treated as a matrix whose elements (pixels) assume a finite number of values then there is a large but finite set of possible images that can be transmitted. By treating this finite set as a 'signal' set, digital communications methods may be used to estimate the uncorrupted image given a blurred and noisy version. Specifically, row-by-row estimation, decision-feedback and vector-quantization are used to extend the 1D sequence estimation ability of the a-posteriori probability (APP) and Viterbi algorithm (VA) to the estimation of 2D images. Simulations show the 2D VA and APP algorithms return near-optimal estimates of binary images as well as improved estimates of greyscale images when compared with the conventional Wiener filter (WF) estimates. Unlike the WF, the VA and APP algorithms are shown to be capable of super-resolution and adaptable for use with signal-dependent Poisson noise corruption. Restorations of experimental data gathered from an optical imaging system are presented to support simulation results.
dc.language.isoen_USen_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.subjectEngineering, Electronics and Electrical.en_US
dc.subjectPhysics, Optics.en_US
dc.titleImage restoration using trellis-search methodsen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest9927465en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b39560107en_US
refterms.dateFOA2018-04-26T08:02:14Z
html.description.abstractMethods for the restoration of images corrupted by blur and noise are presented. During transmission through an optical or electrical channel, images become corrupted by blur and noise as a result of channel limitations (i.e. optical aberrations or a bandlimit). If images are treated as a matrix whose elements (pixels) assume a finite number of values then there is a large but finite set of possible images that can be transmitted. By treating this finite set as a 'signal' set, digital communications methods may be used to estimate the uncorrupted image given a blurred and noisy version. Specifically, row-by-row estimation, decision-feedback and vector-quantization are used to extend the 1D sequence estimation ability of the a-posteriori probability (APP) and Viterbi algorithm (VA) to the estimation of 2D images. Simulations show the 2D VA and APP algorithms return near-optimal estimates of binary images as well as improved estimates of greyscale images when compared with the conventional Wiener filter (WF) estimates. Unlike the WF, the VA and APP algorithms are shown to be capable of super-resolution and adaptable for use with signal-dependent Poisson noise corruption. Restorations of experimental data gathered from an optical imaging system are presented to support simulation results.


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