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Lossy Compressive Sensing Based on Online Dictionary Learning

dc.authorscopusid 57219399185
dc.authorscopusid 57202685744
dc.authorwosid Ulku,, Irem/Ahd-8857-2022
dc.authorwosid Kızgut, Ersin/M-3074-2018
dc.contributor.author Ulku, Irem
dc.contributor.author Kizgut, Ersin
dc.contributor.authorID 17575 tr_TR
dc.date.accessioned 2020-03-09T13:12:05Z
dc.date.available 2020-03-09T13:12:05Z
dc.date.issued 2019
dc.department Çankaya University en_US
dc.department-temp [Ulku, Irem] Cankaya Univ, Dept Elect & Elect Engn, Eskisehir Yolu 29 Km, TR-06790 Ankara, Turkey; [Kizgut, Ersin] Univ Politecn Valencia, IUMPA, E-46071 Valencia, Spain en_US
dc.description.abstract In 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.description.sponsorship Turkish Scientific and Technical Research Council en_US
dc.description.sponsorship The authors would like to thank the anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the paper. The authors would also like to thank Prof. Dr. Halil T. Eyyuboglu for useful suggestions and comments. This research was partially supported by the Turkish Scientific and Technical Research Council. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citation Ulku, 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.doi 10.31577/cai_2019_1_151
dc.identifier.endpage 172 en_US
dc.identifier.issn 1335-9150
dc.identifier.issue 1 en_US
dc.identifier.scopus 2-s2.0-85072624741
dc.identifier.scopusquality Q4
dc.identifier.startpage 151 en_US
dc.identifier.uri https://doi.org/10.31577/cai_2019_1_151
dc.identifier.volume 38 en_US
dc.identifier.wos WOS:000466028900006
dc.identifier.wosquality Q4
dc.language.iso en en_US
dc.publisher Slovak Acad Sciences inst informatics en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 3
dc.subject Hyperspectral Imaging en_US
dc.subject Compression Algorithms en_US
dc.subject Dictionary Learning en_US
dc.subject Sparse Coding en_US
dc.title Lossy Compressive Sensing Based on Online Dictionary Learning tr_TR
dc.title Lossy Compressive Sensing Based on Online Dictionary Learning en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication

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