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dc.contributor.authorKose, K.en_US
dc.contributor.authorGunay, O.en_US
dc.contributor.authorCetin, A. E.en_US
dc.date.accessioned2015/07/28en_US
dc.date.accessioned2015-07-28T12:03:54Z
dc.date.available2015-07-28T12:03:54Z
dc.date.issued2014-01en_US
dc.identifier.citationKose, K., Gunay, O., & Cetin, A. E. (2014). Compressive sensing using the modified entropy functional. Digital Signal Processing, 24, 63-70.en_US
dc.identifier.issn1051-2004en_US
dc.identifier.urihttp://hdl.handle.net/11693/12922
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractIn most compressive sensing problems, 1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleDigital Signal Processingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.dsp.2013.09.010en_US
dc.rightsCopyright © 2013 Elsevier Inc.en_US
dc.subjectCompressive Sensingen_US
dc.subjectModified Entropy Functionalen_US
dc.subjectProjection Onto Convex Setsen_US
dc.subjectIterative Row-action Methodsen_US
dc.subjectBregman-projection Proximal Splittingen_US
dc.titleCompressive sensing using the modified entropy functionalen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage63en_US
dc.citation.epage70en_US
dc.citation.volumeNumber24en_US
dc.identifier.doi10.1016/j.dsp.2013.09.010en_US
dc.publisherElsevieren_US


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