ENTROPY AND INFORMATION IN THE DESIGN AND ANALYSIS OF IMAGING SYSTEMS.

Persistent Link:
http://hdl.handle.net/10150/184636
Title:
ENTROPY AND INFORMATION IN THE DESIGN AND ANALYSIS OF IMAGING SYSTEMS.
Author:
SABET-PEYMAN, FARHANG.
Issue Date:
1982
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:
The main thrust of this dissertation is the application of statistics and information theory to design, analysis and estimation pertaining to image-forming systems. This study explores the application of Shannon's information in pupil design, the characterization of noise, and study of its behavior in a specific electro-optical system, and estimation of the degraded spread function in atmospherical imagery using the maximum entropy method. Our study shows that a pupil designed to maximize Shannon's information throughput is an apodizer, resulting in resolution and contrast enhancement when compared to the diffraction-limited case. The Strehl ratio is about 0.55. Investigation of statistical and spectral properties as a function of gray level in an electro-optical tracking system indicates that the noise is "white," having a wide band and a close-to-Gaussian distribution. Estimating the spread function via maximum entropy technique has revealed some remarkable results. Using an edge as the object, simulation studies predict a superior estimate in the mean squared error sense to those of the least squares in the presence of three types of noise (signal-dependent Gaussian and Poisson, and signal-independent Gaussian noise). Information theory, linear systems theory, sampling theory and more particularly, statistics and the Fast Fourier Transform are used to derive our results.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Imaging systems -- Design.; Entropy (Information theory); Information theory.; Optical data processing -- Mathematical models.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Optical Sciences; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Frieden, Roy

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleENTROPY AND INFORMATION IN THE DESIGN AND ANALYSIS OF IMAGING SYSTEMS.en_US
dc.creatorSABET-PEYMAN, FARHANG.en_US
dc.contributor.authorSABET-PEYMAN, FARHANG.en_US
dc.date.issued1982en_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.abstractThe main thrust of this dissertation is the application of statistics and information theory to design, analysis and estimation pertaining to image-forming systems. This study explores the application of Shannon's information in pupil design, the characterization of noise, and study of its behavior in a specific electro-optical system, and estimation of the degraded spread function in atmospherical imagery using the maximum entropy method. Our study shows that a pupil designed to maximize Shannon's information throughput is an apodizer, resulting in resolution and contrast enhancement when compared to the diffraction-limited case. The Strehl ratio is about 0.55. Investigation of statistical and spectral properties as a function of gray level in an electro-optical tracking system indicates that the noise is "white," having a wide band and a close-to-Gaussian distribution. Estimating the spread function via maximum entropy technique has revealed some remarkable results. Using an edge as the object, simulation studies predict a superior estimate in the mean squared error sense to those of the least squares in the presence of three types of noise (signal-dependent Gaussian and Poisson, and signal-independent Gaussian noise). Information theory, linear systems theory, sampling theory and more particularly, statistics and the Fast Fourier Transform are used to derive our results.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectImaging systems -- Design.en_US
dc.subjectEntropy (Information theory)en_US
dc.subjectInformation theory.en_US
dc.subjectOptical data processing -- Mathematical models.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorFrieden, Royen_US
dc.identifier.proquest8227366en_US
dc.identifier.oclc682960311en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.