Persistent Link:
http://hdl.handle.net/10150/281981
Title:
NEW METHODS OF NONLINEAR DIGITAL IMAGE RESTORATION
Author:
Hawman, Eric Grant
Issue Date:
1981
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 we develop four new methods for image restoration. The common feature of all these methods is that the object estimates have a nonlinear dependence on the image data and that iterative methods of solution are needed. The restoration algorithms have been compared with some previously developed methods by means of computer simulations. The problem of restoring noisy images where the spread function is known is treated in two ways. First, this restoration problem is regarded as a constrained least squares optimization problem. Different methods of enforcing smoothness on the restoration are considered. It is shown that the use of an arc length penalty function permits better restoration of edges than can be obtained by pure quadratic penalty functions. We also treat some methods for enforcing upper and lower bounds on the restoration. The second approach taken on the known spread function restoration problem is statistical. Here we consider the image forming system as a communication channel in which the unknown object to be estimated is one member from a random ensemble. We propose a new approach to restoration based on maximum entropy methods. This new approach allows one to easily synthesize estimators to comply with various prior constraints the image restorer wishes to impose. We show how this new maximum entropy synthesis procedure relates to previous uses of maximum entropy principles for the restoration problem. The problem of restoring atmospherically degraded images is treated in Chapter 4. Here, in addition to random noise in the image, we are faced with a randomly changing spread function. We formulated two algorithms for restoration that have better noise immunity than any previously proposed methods. Both proposed methods are based on processing a series of short exposure speckle images. The first method is an ad hoc successive least squares estimation procedure which uses the second order moments of the image and the spread function discrete Fourier transforms (DFT). The second method, which performs even better than the first, is a maximum likelihood estimation algorithm to find the object's DFT. The maximum likelihood algorithm uses both the first and second moments of the transfer function and the image's DFT.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Optical images.; Digital filters (Mathematics)
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.titleNEW METHODS OF NONLINEAR DIGITAL IMAGE RESTORATIONen_US
dc.creatorHawman, Eric Granten_US
dc.contributor.authorHawman, Eric Granten_US
dc.date.issued1981en_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 we develop four new methods for image restoration. The common feature of all these methods is that the object estimates have a nonlinear dependence on the image data and that iterative methods of solution are needed. The restoration algorithms have been compared with some previously developed methods by means of computer simulations. The problem of restoring noisy images where the spread function is known is treated in two ways. First, this restoration problem is regarded as a constrained least squares optimization problem. Different methods of enforcing smoothness on the restoration are considered. It is shown that the use of an arc length penalty function permits better restoration of edges than can be obtained by pure quadratic penalty functions. We also treat some methods for enforcing upper and lower bounds on the restoration. The second approach taken on the known spread function restoration problem is statistical. Here we consider the image forming system as a communication channel in which the unknown object to be estimated is one member from a random ensemble. We propose a new approach to restoration based on maximum entropy methods. This new approach allows one to easily synthesize estimators to comply with various prior constraints the image restorer wishes to impose. We show how this new maximum entropy synthesis procedure relates to previous uses of maximum entropy principles for the restoration problem. The problem of restoring atmospherically degraded images is treated in Chapter 4. Here, in addition to random noise in the image, we are faced with a randomly changing spread function. We formulated two algorithms for restoration that have better noise immunity than any previously proposed methods. Both proposed methods are based on processing a series of short exposure speckle images. The first method is an ad hoc successive least squares estimation procedure which uses the second order moments of the image and the spread function discrete Fourier transforms (DFT). The second method, which performs even better than the first, is a maximum likelihood estimation algorithm to find the object's DFT. The maximum likelihood algorithm uses both the first and second moments of the transfer function and the image's DFT.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectOptical images.en_US
dc.subjectDigital filters (Mathematics)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.proquest8118458en_US
dc.identifier.oclc8679985en_US
dc.identifier.bibrecord.b1805965xen_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.