Novel super-resolution algorithms and enhanced noise removal algorithm for image restoration systems and applications

Persistent Link:
http://hdl.handle.net/10150/279978
Title:
Novel super-resolution algorithms and enhanced noise removal algorithm for image restoration systems and applications
Author:
Pang, Ho-Yuen
Issue Date:
2002
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:
This dissertation is concerned with the introduction of a systematic way of modeling image processing. A dynamic imaging system model constructed from an information theory framework is proposed. Unlike an earlier simple model, the proposed dynamic imaging system (DIS) model is suitable for a wide range of applications. This DIS model is inspired by the Shannon communication theory. The Shannon communication theory is credited for the rapid development of the communication industry. Currently, most image processing researchers focus on developing fast algorithms and better hardware. An information theoretic-based approach to image processing could bring as large an impact to the image processing area as Shannon's communication theory had on the communications area. This proposed DIS model will use the information obtained from the acquired images to provide an estimation of the unknown atmospheric turbulence, vibration, etc. It will also automatically adjust the sampling rate, wavelength band, and algorithms of choice, to produce the best possible restored image with limited information under uncertainty. This dissertation develops the concept of the DIS model including its basic components. We have implemented three parts of this system. First, we implemented a noise removal algorithm based on the Markov random field (MRF). It is shown that this algorithm achieves better performance than other MRF-based algorithms in noise removal. Second, we have implemented a hybrid maximum likelihood/projection-on-convex-set image restoration algorithm and demonstrate that it outperforms the maximum likelihood algorithm. Third, we have implemented a self-organized map-based image restoration algorithm and compare its performance to several well-known methods. It can be implemented in parallel processing to achieve super-resolution in real time without performing a time consuming iteration process. The impact of the development of these DIS system critical components is discussed and future research areas are elucidated.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Ziolkowski, Richard W.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleNovel super-resolution algorithms and enhanced noise removal algorithm for image restoration systems and applicationsen_US
dc.creatorPang, Ho-Yuenen_US
dc.contributor.authorPang, Ho-Yuenen_US
dc.date.issued2002en_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.abstractThis dissertation is concerned with the introduction of a systematic way of modeling image processing. A dynamic imaging system model constructed from an information theory framework is proposed. Unlike an earlier simple model, the proposed dynamic imaging system (DIS) model is suitable for a wide range of applications. This DIS model is inspired by the Shannon communication theory. The Shannon communication theory is credited for the rapid development of the communication industry. Currently, most image processing researchers focus on developing fast algorithms and better hardware. An information theoretic-based approach to image processing could bring as large an impact to the image processing area as Shannon's communication theory had on the communications area. This proposed DIS model will use the information obtained from the acquired images to provide an estimation of the unknown atmospheric turbulence, vibration, etc. It will also automatically adjust the sampling rate, wavelength band, and algorithms of choice, to produce the best possible restored image with limited information under uncertainty. This dissertation develops the concept of the DIS model including its basic components. We have implemented three parts of this system. First, we implemented a noise removal algorithm based on the Markov random field (MRF). It is shown that this algorithm achieves better performance than other MRF-based algorithms in noise removal. Second, we have implemented a hybrid maximum likelihood/projection-on-convex-set image restoration algorithm and demonstrate that it outperforms the maximum likelihood algorithm. Third, we have implemented a self-organized map-based image restoration algorithm and compare its performance to several well-known methods. It can be implemented in parallel processing to achieve super-resolution in real time without performing a time consuming iteration process. The impact of the development of these DIS system critical components is discussed and future research areas are elucidated.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorZiolkowski, Richard W.en_US
dc.identifier.proquest3050345en_US
dc.identifier.bibrecord.b42728216en_US
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