Model-Based Acquisition for Compressive Sensing & Imaging

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dc.contributor.advisorKelly, Kevin F.en_US
dc.contributor.committeeMemberBaraniuk, Richard G.en_US
dc.contributor.committeeMemberYin, Wotaoen_US
dc.creatorLi, Yunen_US
dc.date.accessioned2013-09-16T15:19:34Zen_US
dc.date.accessioned2013-09-16T15:19:37Zen_US
dc.date.available2014-03-19T05:10:04Zen_US
dc.date.created2013-05en_US
dc.date.issued2013-09-16en_US
dc.date.submittedMay 2013en_US
dc.date.updated2013-09-16T15:19:38Zen_US
dc.description.abstractCompressive sensing (CS) is a novel imaging technology based on the inherent redundancy of natural scenes. The minimum number of required measurements which defines the maximum image compression rate is lower-bounded by the sparsity of the image but is dependent on the type of acquisition patterns employed. Increased measurements by the Rice single pixel camera (SPC) slows down the acquisition process, which may cause the image recovery to be more susceptible to background noise and thus limit CS's application in imaging, detection, or classifying moving targets. In this study, two methods (hybrid-subspace sparse sampling (HSS) for imaging and secant projection on a manifold for classification are applied to solving this problem. For the HSS method, new image pattern are designed via robust principle component analysis (rPCA) on prior knowledge from a library of images to sense a common structure. After measuring coarse scale commonalities, the residual image becomes sparser, and then fewer measurements are needed. For the secant projection case, patterns that can preserve the pairwise distance between data points based on manifold learning are designed via semi-definite programming. These secant patterns turn out to be better in object classification over those learned from PCA. Both methods considerably decrease the number of required measurements for each task when compared with the purely random patterns of a more universal CS imaging system.en_US
dc.embargo.terms2014-03-16T05:00:00Zen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationLi, Yun. "Model-Based Acquisition for Compressive Sensing & Imaging." (2013) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/71977">https://hdl.handle.net/1911/71977</a>.en_US
dc.identifier.slug123456789/ETD-2013-05-497en_US
dc.identifier.urihttps://hdl.handle.net/1911/71977en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectElectrical engineeringen_US
dc.subjectOpticsen_US
dc.subjectSensingen_US
dc.subjectCompressive sensingen_US
dc.subjectCompressive imagingen_US
dc.subjectClassificationen_US
dc.subjectManifold learningen_US
dc.subjectDimensional reductionen_US
dc.subjectComputer engineeringen_US
dc.titleModel-Based Acquisition for Compressive Sensing & Imagingen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentApplied Physicsen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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