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Image compression and recovery using compressive sampling and particle swarm optimization.

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dc.contributor.advisor Sturgill, David Brian.
dc.contributor.author Van Ruitenbeek, Benjamin D.
dc.contributor.other Baylor University. Dept. of Computer Science. en
dc.date.copyright 2009-08
dc.identifier.uri http://hdl.handle.net/2104/5397
dc.description.abstract We present a novel method for sparse signal recovery using Particle Swarm Optimization and demonstrate an application in image compression. Images are compressed with compressive sampling, and then reconstructed with particle swarm techniques. Several enhancements to the basic particle swarm algorithm are shown to improve signal recovery accuracy. We also present techniques specifically for reconstructing sparse image data and evaluate their performance. en
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission. en
dc.subject Image compression. en
dc.subject Mathematical optimization -- Computer programs. en
dc.subject Swarm intelligence. en
dc.title Image compression and recovery using compressive sampling and particle swarm optimization. en
dc.type Thesis en
dc.description.degree M.S. en
dc.rights.accessrights Worldwide access en
dc.contributor.department Computer Science. en


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