Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples
Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l 0 norm, even though, in practice, the l 1 or the l p ( 0
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| Veröffentlicht in: | IEEE journal of selected topics in signal processing 2011-09, Vol.5 (5), p.927-932 |
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| container_title | IEEE journal of selected topics in signal processing |
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| creator | Zonoobi, D. Kassim, A. A. Venkatesh, Y. V. |
| description | Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l 0 norm, even though, in practice, the l 1 or the l p ( 0 |
| doi_str_mv | 10.1109/JSTSP.2011.2160711 |
| format | Article |
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A. ; Venkatesh, Y. V.</creator><creatorcontrib>Zonoobi, D. ; Kassim, A. A. ; Venkatesh, Y. V.</creatorcontrib><description>Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l 0 norm, even though, in practice, the l 1 or the l p ( 0 <; p <; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity for a significantly improved performance in its reconstruction from compressive samples. We also successfully incorporate the GI into a stochastic optimization algorithm for signal reconstruction from compressive samples and illustrate our approach with both synthetic and real signals/images.</description><identifier>ISSN: 1932-4553</identifier><identifier>EISSN: 1941-0484</identifier><identifier>DOI: 10.1109/JSTSP.2011.2160711</identifier><identifier>CODEN: IJSTGY</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Compressive sensing (CS) ; Gini index (GI) ; Image coding ; Image reconstruction ; Minimization ; Noise ; Noise measurement ; non-convex optimization ; Norms ; Optimization ; Sampling ; Signal processing ; Signal reconstruction ; simultaneous perturbation stochastic approximation (SPSA) ; sparsity measures ; Stochasticity ; Transforms</subject><ispartof>IEEE journal of selected topics in signal processing, 2011-09, Vol.5 (5), p.927-932</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Sep 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-1cc36ed12f018cb8bf9e37c25d6fb0d3ad0fc8dd271e7031863d6ca32c1a46cc3</citedby><cites>FETCH-LOGICAL-c393t-1cc36ed12f018cb8bf9e37c25d6fb0d3ad0fc8dd271e7031863d6ca32c1a46cc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5934357$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5934357$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zonoobi, D.</creatorcontrib><creatorcontrib>Kassim, A. A.</creatorcontrib><creatorcontrib>Venkatesh, Y. V.</creatorcontrib><title>Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples</title><title>IEEE journal of selected topics in signal processing</title><addtitle>JSTSP</addtitle><description>Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l 0 norm, even though, in practice, the l 1 or the l p ( 0 <; p <; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity for a significantly improved performance in its reconstruction from compressive samples. We also successfully incorporate the GI into a stochastic optimization algorithm for signal reconstruction from compressive samples and illustrate our approach with both synthetic and real signals/images.</description><subject>Algorithms</subject><subject>Compressive sensing (CS)</subject><subject>Gini index (GI)</subject><subject>Image coding</subject><subject>Image reconstruction</subject><subject>Minimization</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>non-convex optimization</subject><subject>Norms</subject><subject>Optimization</subject><subject>Sampling</subject><subject>Signal processing</subject><subject>Signal reconstruction</subject><subject>simultaneous perturbation stochastic approximation (SPSA)</subject><subject>sparsity measures</subject><subject>Stochasticity</subject><subject>Transforms</subject><issn>1932-4553</issn><issn>1941-0484</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkD1PwzAQQCMEElD4A7BYTCwpPjsfzogqKEUgPlLmyLXPyFUSBztB9N-TUsTAdDe8d9K9KDoDOgWgxdV9uSyfp4wCTBlkNAfYi46gSCCmiUj2tztncZKm_DA6DmFNaZpnkBxFL3PbWrJoNX4RGUjZSR9svyGPKMPgkRjnSWnfW1mTV1SuDb0fVG9dS4x3DZm5pvMYgv1EUsqmqzGcRAdG1gFPf-ckeru9Wc7u4oen-WJ2_RArXvA-BqV4hhqYoSDUSqxMgTxXLNWZWVHNpaZGCa1ZDphTDiLjOlOSMwUyyUZ5El3u7nbefQwY-qqxQWFdyxbdECoYJU5pTumIXvxD127w40-hEoILToGJEWI7SHkXgkdTdd420m_GS9U2cvUTudpGrn4jj9L5TrKI-CekBU94mvNvWd147A</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Zonoobi, D.</creator><creator>Kassim, A. A.</creator><creator>Venkatesh, Y. V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>201109</creationdate><title>Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples</title><author>Zonoobi, D. ; Kassim, A. A. ; Venkatesh, Y. 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A.</creatorcontrib><creatorcontrib>Venkatesh, Y. V.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE journal of selected topics in signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zonoobi, D.</au><au>Kassim, A. A.</au><au>Venkatesh, Y. V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples</atitle><jtitle>IEEE journal of selected topics in signal processing</jtitle><stitle>JSTSP</stitle><date>2011-09</date><risdate>2011</risdate><volume>5</volume><issue>5</issue><spage>927</spage><epage>932</epage><pages>927-932</pages><issn>1932-4553</issn><eissn>1941-0484</eissn><coden>IJSTGY</coden><abstract>Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l 0 norm, even though, in practice, the l 1 or the l p ( 0 <; p <; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity for a significantly improved performance in its reconstruction from compressive samples. 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| subjects | Algorithms Compressive sensing (CS) Gini index (GI) Image coding Image reconstruction Minimization Noise Noise measurement non-convex optimization Norms Optimization Sampling Signal processing Signal reconstruction simultaneous perturbation stochastic approximation (SPSA) sparsity measures Stochasticity Transforms |
| title | Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples |
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