Compressed sensing under strong noise. Application to imaging through multiply scattering media
Identifieur interne : 001728 ( Hal/Curation ); précédent : 001727; suivant : 001729Compressed sensing under strong noise. Application to imaging through multiply scattering media
Auteurs : Antoine Liutkus [France] ; David Martina [France] ; Sylvain Gigan [France] ; Laurent Daudet [France]Source :
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Abstract
Compressive sensing exploits the structure of signals to acquire them with fewer measurements than required by the Nyquist-Shannon theory. However, the design of practical compressive sensing hardware raises several issues. First, one has to elicit a measurement mechanism that exhibits adequate incoherence properties. Second, the system should be robust to noise, whether it be measurement noise, or calibration noise, i.e. discrepancies between theoretical and actual measurement matrices. Third, to improve performance in the case of strong noise, it is not clear whether one should increase the number of sensors, or rather take several mea-surements, thus settling in the multiple measurement vector scenario (MMV). Here, we first show how measurement matrices may be estimated by calibration instead of being assumed perfectly known, and second that if the noise level reaches a few percents of the signal level, MMV is the only way to sample sparse signals at sub-Nyquist sampling rates.
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Paris 6</orgName><orgName type="acronym">UPMC</orgName><desc><address><addrLine>4 place Jussieu - 75005 Paris</addrLine><country key="FR"></country></address><ref type="url">http://www.upmc.fr/</ref></desc></org></tutelle><tutelle active="#struct-300301" type="direct"><org type="institution" xml:id="struct-300301" status="VALID"><orgName>Université Paris Diderot - Paris 7</orgName><orgName type="acronym">UP7</orgName><desc><address><addrLine>5 rue Thomas-Mann - 75205 Paris cedex 13</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-paris-diderot.fr</ref></desc></org></tutelle><tutelle name="UMR7587" active="#struct-441569" type="direct"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle><tutelle active="#struct-301585" type="direct"><org type="institution" xml:id="struct-301585" status="VALID"><orgName>ESPCI ParisTech</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>MMV</term><term>calibration</term><term>compressive sensing</term><term>experimental study</term><term>optical imaging</term><term>scattering media</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Compressive sensing exploits the structure of signals to acquire them with fewer measurements than required by the Nyquist-Shannon theory. However, the design of practical compressive sensing hardware raises several issues. First, one has to elicit a measurement mechanism that exhibits adequate incoherence properties. Second, the system should be robust to noise, whether it be measurement noise, or calibration noise, i.e. discrepancies between theoretical and actual measurement matrices. Third, to improve performance in the case of strong noise, it is not clear whether one should increase the number of sensors, or rather take several mea-surements, thus settling in the multiple measurement vector scenario (MMV). Here, we first show how measurement matrices may be estimated by calibration instead of being assumed perfectly known, and second that if the noise level reaches a few percents of the signal level, MMV is the only way to sample sparse signals at sub-Nyquist sampling rates.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Compressed sensing under strong noise. Application to imaging through multiply scattering media</title><author role="aut"><persName><forename type="first">Antoine</forename><surname>Liutkus</surname></persName><email></email><idno type="idhal">antoine-liutkus</idno><idno type="halauthor">628142</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">David</forename><surname>Martina</surname></persName><email></email><idno type="halauthor">95286</idno><affiliation ref="#struct-55414"></affiliation><affiliation ref="#struct-129664"></affiliation></author><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Gigan</surname></persName><email></email><idno type="halauthor">3743</idno><affiliation ref="#struct-55414"></affiliation><affiliation ref="#struct-129664"></affiliation></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Daudet</surname></persName><email></email><idno type="halauthor">136175</idno><affiliation ref="#struct-129664"></affiliation></author><editor role="depositor"><persName><forename>Antoine</forename><surname>Liutkus</surname></persName><email>antoine@liutkus.net</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-10-15 14:37:09</date><date type="whenModified">2016-05-18 08:55:24</date><date type="whenReleased">2014-10-30 15:53:10</date><date type="whenProduced">2014-09-01</date><date type="whenEndEmbargoed">2014-10-15</date><ref type="file" target="https://hal.inria.fr/hal-01074786/document"><date notBefore="2014-10-15"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01074786/file/EUSIPCO-CS.pdf"><date notBefore="2014-10-15"></date></ref></edition><respStmt><resp>contributor</resp><name key="167122"><persName><forename>Antoine</forename><surname>Liutkus</surname></persName><email>antoine@liutkus.net</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01074786</idno><idno type="halUri">https://hal.inria.fr/hal-01074786</idno><idno type="halBibtex">liutkus:hal-01074786</idno><idno type="halRefHtml">European Signal Processing Conference (EUSIPCO), Sep 2014, Lisbon, Portugal. 2014</idno><idno type="halRef">European Signal Processing Conference (EUSIPCO), Sep 2014, Lisbon, Portugal. 2014</idno></publicationStmt><seriesStmt><idno type="stamp" n="UNIV-PARIS7">Université Denis Diderot - Paris VII</idno><idno type="stamp" n="UPMC">Université Pierre et Marie Curie</idno><idno type="stamp" n="ESPCI" p="PARISTECH">ESPCI ParisTech</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="IL" p="ESPCI">Institut Langevin</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LANGEVIN" p="UPMC">Institut Langevin "Ondes et Images"</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="PARISTECH">ParisTech</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Compressed sensing under strong noise. Application to imaging through multiply scattering media</title><author role="aut"><persName><forename type="first">Antoine</forename><surname>Liutkus</surname></persName><idno type="idHal">antoine-liutkus</idno><idno type="halAuthorId">628142</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">David</forename><surname>Martina</surname></persName><idno type="halAuthorId">95286</idno><affiliation ref="#struct-55414"></affiliation><affiliation ref="#struct-129664"></affiliation></author><author role="aut"><persName><forename type="first">Sylvain</forename><surname>Gigan</surname></persName><idno type="halAuthorId">3743</idno><affiliation ref="#struct-55414"></affiliation><affiliation ref="#struct-129664"></affiliation></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Daudet</surname></persName><idno type="halAuthorId">136175</idno><affiliation ref="#struct-129664"></affiliation></author></analytic><monogr><meeting><title>European Signal Processing Conference (EUSIPCO)</title><date type="start">2014-09-01</date><date type="end">2014-09-05</date><settlement>Lisbon</settlement><country key="PT">Portugal</country></meeting><imprint><date type="datePub">2014-09-01</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">compressive sensing</term><term xml:lang="en">calibration</term><term xml:lang="en">MMV</term><term xml:lang="en">experimental study</term><term xml:lang="en">optical imaging</term><term xml:lang="en">scattering media</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Compressive sensing exploits the structure of signals to acquire them with fewer measurements than required by the Nyquist-Shannon theory. However, the design of practical compressive sensing hardware raises several issues. First, one has to elicit a measurement mechanism that exhibits adequate incoherence properties. Second, the system should be robust to noise, whether it be measurement noise, or calibration noise, i.e. discrepancies between theoretical and actual measurement matrices. Third, to improve performance in the case of strong noise, it is not clear whether one should increase the number of sensors, or rather take several mea-surements, thus settling in the multiple measurement vector scenario (MMV). Here, we first show how measurement matrices may be estimated by calibration instead of being assumed perfectly known, and second that if the noise level reaches a few percents of the signal level, MMV is the only way to sample sparse signals at sub-Nyquist sampling rates.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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