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Lossy Compressive Sensing Based on Online Dictionary Learning

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dc.contributor.authorÜlkü, İrem
dc.contributor.authorKizgut, Ersin
dc.contributor.authorID17575tr_TR
dc.date.accessioned2020-03-09T13:12:05Z
dc.date.available2020-03-09T13:12:05Z
dc.date.issued2019
dc.departmentÇankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.en_US
dc.identifier.citationUlku, Irem; Kizgut, Ersin, "Lossy Compressive Sensing Based on Online Dictionary Learning", Computing and Informatics, Vol. 38, No. 1, pp. 151-172, (2019).en_US
dc.identifier.doi10.31577/cai_2019_1_151
dc.identifier.endpage172en_US
dc.identifier.issn1335-9150
dc.identifier.issue1en_US
dc.identifier.startpage151en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/2624
dc.identifier.volume38en_US
dc.language.isoenen_US
dc.publisherSlovak Acad Sciences Inst Informaticsen_US
dc.relation.ispartofComputing and Informaticsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral Imagingen_US
dc.subjectCompression Algorithmsen_US
dc.subjectDictionary Learningen_US
dc.subjectSparse Codingen_US
dc.titleLossy Compressive Sensing Based on Online Dictionary Learningtr_TR
dc.titleLossy Compressive Sensing Based on Online Dictionary Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication

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