Parallel image restoration

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buir.advisorAykanat, Cevdet
dc.contributor.authorMalas, Tahir
dc.date.accessioned2016-07-01T10:59:50Z
dc.date.available2016-07-01T10:59:50Z
dc.date.issued2004
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractIn this thesis, we are concerned with the image restoration problem which has been formulated in the literature as a system of linear inequalities. With this formulation, the resulting constraint matrix is an unstructured sparse-matrix and even with small size images we end up with huge matrices. So, to solve the restoration problem, we have used the surrogate constraint methods, that can work efficiently for large size problems and are amenable for parallel implementations. Among the surrogate constraint methods, the basic method considers all of the violated constraints in the system and performs a single block projection in each step. On the other hand, parallel method considers a subset of the constraints, and makes simultaneous block projections. Using several partitioning strategies and adopting different communication models we have realized several parallel implementations of the two methods. We have used the hypergraph partitioning based decomposition methods in order to minimize the communication costs while ensuring load balance among the processors. The implementations are evaluated based on the per iteration performance and on the overall performance. Besides, the effects of different partitioning strategies on the speed of convergence are investigated. The experimental results reveal that the proposed parallelization schemes have practical usage in the restoration problem and in many other real-world applications which can be modeled as a system of linear inequalities.en_US
dc.description.provenanceMade available in DSpace on 2016-07-01T10:59:50Z (GMT). No. of bitstreams: 1 0002482.pdf: 708778 bytes, checksum: 1ab738fab79d9326d751e66864fbb4f8 (MD5) Previous issue date: 2004en
dc.description.statementofresponsibilityMalas, Tahiren_US
dc.format.extentxi, 90 leaves, 30 cmen_US
dc.identifier.itemidBILKUTUPB080179
dc.identifier.urihttp://hdl.handle.net/11693/29451
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParallel image restorationen_US
dc.subjectconvergence rateen_US
dc.subjectall-to-all communicationen_US
dc.subjectpoint-to-point communicationen_US
dc.subjectfine-grain partitioningen_US
dc.subjectcheckerboard partitioningen_US
dc.subjectrowwise partitioningen_US
dc.subjecthypergraph partitioningen_US
dc.subjectsurrogate constraint methoden_US
dc.subjectlinear feasibilityen_US
dc.subjectparallel algorithmsen_US
dc.subjectdistortionen_US
dc.subject.lccTA1637 .M35 2004en_US
dc.subject.lcshImage processing Digital techniques.en_US
dc.titleParallel image restorationen_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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