Persistent Link:
http://hdl.handle.net/10150/183994
Title:
RESTORATION FOR SAMPLED IMAGING SYSTEMS.
Author:
WOOD, LYNNETTE.
Issue Date:
1986
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:
Digital image restoration requires some knowledge of the degradation phenomena in order to attempt an inversion of that degradation. Typically, degradations which are included in the restoration process are those resulting from the optics and electronics of the imaging device. Occasionally, blurring caused by an intervening atmosphere, uniform motion or defocused optics is also included. Recently it has been shown that sampling, the conversion of the continuous output of an imaging system to a discrete array, further degrades or blurs the image. Thus, incorporating sampling effects into the restoration should improve the quality of the restored image. The system transfer function (the Fourier transform of the point spread function), was derived for the Landset Multi-Spectral Scanner and Thematic Mapper systems. Sampling effects were included, along with the relevant optical, instantaneous field of view and electronic filter data, in the system analysis. Using the system transfer function, a least squares (Wiener) filter was then derived. A Wiener filter requires the ratio of the power spectra of the scene and noise, which is often, for simplicity, assumed to be a constant over frequency. The restoration method used here includes models for the power spectra which are based on the study of several different types of Landsat scenes. The Wiener filter is then inverse Fourier transformed to find a restoration filter which is spatially windowed to suppress ringing. Qualitative and quantitative evaluations are made of the restored imagery. Comparisons are made to the approaches taken by other investigators, in particular, to one who has had success restoring the same type of imagery. It is found that the restoration method used here compares favorably with this previous work.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Imaging systems.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Applied Mathematics; Graduate College
Degree Grantor:
University of Arizona

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleRESTORATION FOR SAMPLED IMAGING SYSTEMS.en_US
dc.creatorWOOD, LYNNETTE.en_US
dc.contributor.authorWOOD, LYNNETTE.en_US
dc.date.issued1986en_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.abstractDigital image restoration requires some knowledge of the degradation phenomena in order to attempt an inversion of that degradation. Typically, degradations which are included in the restoration process are those resulting from the optics and electronics of the imaging device. Occasionally, blurring caused by an intervening atmosphere, uniform motion or defocused optics is also included. Recently it has been shown that sampling, the conversion of the continuous output of an imaging system to a discrete array, further degrades or blurs the image. Thus, incorporating sampling effects into the restoration should improve the quality of the restored image. The system transfer function (the Fourier transform of the point spread function), was derived for the Landset Multi-Spectral Scanner and Thematic Mapper systems. Sampling effects were included, along with the relevant optical, instantaneous field of view and electronic filter data, in the system analysis. Using the system transfer function, a least squares (Wiener) filter was then derived. A Wiener filter requires the ratio of the power spectra of the scene and noise, which is often, for simplicity, assumed to be a constant over frequency. The restoration method used here includes models for the power spectra which are based on the study of several different types of Landsat scenes. The Wiener filter is then inverse Fourier transformed to find a restoration filter which is spatially windowed to suppress ringing. Qualitative and quantitative evaluations are made of the restored imagery. Comparisons are made to the approaches taken by other investigators, in particular, to one who has had success restoring the same type of imagery. It is found that the restoration method used here compares favorably with this previous work.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectImaging systems.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineApplied Mathematicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.identifier.proquest8708575en_US
dc.identifier.oclc698260541en_US
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