Image super-resolution performance of multilayer feedforward neural networks

Persistent Link:
http://hdl.handle.net/10150/284549
Title:
Image super-resolution performance of multilayer feedforward neural networks
Author:
Davila, Carlos Antonio
Issue Date:
1999
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:
Super-resolution is the process by which the bandwidth of a diffraction-limited spectrum is extended beyond the optical passband. Many algorithms exist which are capable of super-resolution; however most are iterative methods, which are ill-suited for real-time operation. One approach that has been virtually ignored in super-resolution research is the neural network approach. The Hopfield network has been a popular choice in image restoration applications, however it is also an iterative approach. We consider the feedforward architecture known as a Multilayer Perceptron (MLP), and present results on simulated binary and greyscale images blurred by a diffraction-limited OTF and sampled at the Nyquist rate. To avoid aliasing, the network performs as a nonlinear spatial interpolator while simultaneously extrapolating in the frequency domain. Additionally, a novel use of vector quantization for the generation of training data sets is presented. This is accomplished by training a nonlinear vector quantizer (NLIVQ), whose codebooks are subsequently used in the supervised training of the MLP network using Back-Propagation. The network shows good regularization in the presence of noise.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Physics, Optics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Hunt, Bobby R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleImage super-resolution performance of multilayer feedforward neural networksen_US
dc.creatorDavila, Carlos Antonioen_US
dc.contributor.authorDavila, Carlos Antonioen_US
dc.date.issued1999en_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.abstractSuper-resolution is the process by which the bandwidth of a diffraction-limited spectrum is extended beyond the optical passband. Many algorithms exist which are capable of super-resolution; however most are iterative methods, which are ill-suited for real-time operation. One approach that has been virtually ignored in super-resolution research is the neural network approach. The Hopfield network has been a popular choice in image restoration applications, however it is also an iterative approach. We consider the feedforward architecture known as a Multilayer Perceptron (MLP), and present results on simulated binary and greyscale images blurred by a diffraction-limited OTF and sampled at the Nyquist rate. To avoid aliasing, the network performs as a nonlinear spatial interpolator while simultaneously extrapolating in the frequency domain. Additionally, a novel use of vector quantization for the generation of training data sets is presented. This is accomplished by training a nonlinear vector quantizer (NLIVQ), whose codebooks are subsequently used in the supervised training of the MLP network using Back-Propagation. The network shows good regularization in the presence of noise.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectPhysics, Optics.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.advisorHunt, Bobby R.en_US
dc.identifier.proquest9934855en_US
dc.identifier.bibrecord.b39652245en_US
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