Persistent Link:
http://hdl.handle.net/10150/187279
Title:
Applying modified CLEAN algorithm to MAP image super-resolution.
Author:
Yuen, Patrick Wingkee.
Issue Date:
1995
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:
In this dissertation, the super-resolution method that we use for image restoration is the Poisson Maximum A-Posteriori (MAP) super-resolution algorithm of Hunt, computed with an iterative form. This algorithm is similar to the Maximum Likelihood of Holmes, which is derived from an Expectation/Maximization (EM) computation. Image restoration of point source data is our focus. This is because most astronomical data can be regarded as multiple point source data with a very dark background. The statistical limits imposed by photon noise on the resolution obtained by our algorithm are investigated. We improve the performance of the super-resolution algorithm by including the additional information of the spatial constraints. This is achieved by applying the well-known CLEAN algorithm, which is widely used in astronomy, to create regions of support for the potential point sources. Real and simulated data are included in this paper. The point spread function (psf) of a diffraction limited optical system is used for the simulated data. The real data is two dimensional optical image data from the Hubble Space Telescope.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical and Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Hunt, Bobby

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleApplying modified CLEAN algorithm to MAP image super-resolution.en_US
dc.creatorYuen, Patrick Wingkee.en_US
dc.contributor.authorYuen, Patrick Wingkee.en_US
dc.date.issued1995en_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.abstractIn this dissertation, the super-resolution method that we use for image restoration is the Poisson Maximum A-Posteriori (MAP) super-resolution algorithm of Hunt, computed with an iterative form. This algorithm is similar to the Maximum Likelihood of Holmes, which is derived from an Expectation/Maximization (EM) computation. Image restoration of point source data is our focus. This is because most astronomical data can be regarded as multiple point source data with a very dark background. The statistical limits imposed by photon noise on the resolution obtained by our algorithm are investigated. We improve the performance of the super-resolution algorithm by including the additional information of the spatial constraints. This is achieved by applying the well-known CLEAN algorithm, which is widely used in astronomy, to create regions of support for the potential point sources. Real and simulated data are included in this paper. The point spread function (psf) of a diffraction limited optical system is used for the simulated data. The real data is two dimensional optical image data from the Hubble Space Telescope.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairHunt, Bobbyen_US
dc.contributor.committeememberMarcellin, Michaelen_US
dc.contributor.committeememberSchowengerdt, Robert A.en_US
dc.identifier.proquest9604505en_US
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