{"id":"https://openalex.org/W2111297117","doi":"https://doi.org/10.1109/icassp.2013.6638828","title":"Sampling and recovery of continuous sparse signals by maximum likelihood estimation","display_name":"Sampling and recovery of continuous sparse signals by maximum likelihood estimation","publication_year":2013,"publication_date":"2013-05-01","ids":{"openalex":"https://openalex.org/W2111297117","doi":"https://doi.org/10.1109/icassp.2013.6638828","mag":"2111297117"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2013.6638828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2013.6638828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112495604","display_name":"Akira Hirabayashi","orcid":null},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Hirabayashi","raw_affiliation_strings":["Yamaguchi University, Ube, Japan","Yamaguchi University,Ube,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University, Ube, Japan","institution_ids":["https://openalex.org/I173915773"]},{"raw_affiliation_string":"Yamaguchi University,Ube,Japan","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056533545","display_name":"Yosuke Hironaga","orcid":null},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yosuke Hironaga","raw_affiliation_strings":["Yamaguchi University, Ube, Japan","Yamaguchi University,Ube,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University, Ube, Japan","institution_ids":["https://openalex.org/I173915773"]},{"raw_affiliation_string":"Yamaguchi University,Ube,Japan","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024254029","display_name":"Laurent Condat","orcid":"https://orcid.org/0000-0001-7087-1002"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210124956","display_name":"GIPSA-Lab","ror":"https://ror.org/02wrme198","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I4210124956","https://openalex.org/I899635006","https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Laurent Condat","raw_affiliation_strings":["GIPSA-lab, Centre National de la Recherche Scientifique, Grenoble, France","GIPSA-Lab., Grenoble, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GIPSA-lab, Centre National de la Recherche Scientifique, Grenoble, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210124956"]},{"raw_affiliation_string":"GIPSA-Lab., Grenoble, France","institution_ids":["https://openalex.org/I4210124956"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6971,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.84794537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"6058","last_page":"6062"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.7095760107040405},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5779581069946289},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.560523271560669},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5430558919906616},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5290383696556091},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5057268142700195},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4815552532672882},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.47739356756210327},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4452211856842041},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.44479602575302124},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.44426336884498596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43274450302124023},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41853466629981995},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.41603556275367737},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.40747132897377014},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18124917149543762},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12444713711738586}],"concepts":[{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.7095760107040405},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5779581069946289},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.560523271560669},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5430558919906616},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5290383696556091},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5057268142700195},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4815552532672882},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.47739356756210327},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4452211856842041},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.44479602575302124},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.44426336884498596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43274450302124023},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41853466629981995},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.41603556275367737},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.40747132897377014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18124917149543762},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12444713711738586},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2013.6638828","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2013.6638828","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.705.2629","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.705.2629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.gipsa-lab.grenoble-inp.fr/%7Elaurent.condat/publis/hirabayashi_icassp13_pso.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:hal-00936078v1","is_oa":false,"landing_page_url":"https://hal.science/hal-00936078","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ICASSP 2013 - 38th IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, Canada. pp.6058-6062","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2953215040","display_name":"Multidimensional E-spline sampling theory and applications","funder_award_id":"23500212","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W340244495","https://openalex.org/W2054525999","https://openalex.org/W2065754355","https://openalex.org/W2071317135","https://openalex.org/W2103300762","https://openalex.org/W2113885710","https://openalex.org/W2118682956","https://openalex.org/W2119667497","https://openalex.org/W2138787877","https://openalex.org/W2144503328","https://openalex.org/W2149213383","https://openalex.org/W2151995448","https://openalex.org/W2152195021","https://openalex.org/W2158537680","https://openalex.org/W2166493798","https://openalex.org/W2296616510","https://openalex.org/W4214585779","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W2015530857","https://openalex.org/W2556064263","https://openalex.org/W1991846142","https://openalex.org/W1583020711","https://openalex.org/W2385263368","https://openalex.org/W1521151968","https://openalex.org/W2128655648","https://openalex.org/W2900672867","https://openalex.org/W1690802106","https://openalex.org/W2033368883"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,118],"maximum":[3],"likelihood":[4],"estimation":[5],"approach":[6,30,80],"for":[7,31,117],"the":[8,50,58,87,95,105,111,114],"recovery":[9],"of":[10,21,23,52,60,83,98,113],"continuously-defined":[11],"sparse":[12],"signals":[13],"from":[14],"noisy":[15],"measurements,":[16],"in":[17],"particular":[18],"periodic":[19],"sequences":[20],"derivatives":[22,59],"Diracs":[24,61,64],"and":[25,42,55],"piecewise":[26],"polynomials.":[27],"The":[28,77],"conventional":[29],"this":[32],"problem":[33],"is":[34,91],"based":[35],"on":[36],"total-least-squares":[37],"(a.k.a.":[38],"annihilating":[39],"filter":[40],"method)":[41],"Cadzow":[43,70],"denoising.":[44],"It":[45],"requires":[46],"more":[47],"measurements":[48],"than":[49],"number":[51],"unknown":[53],"parameters":[54],"mistakenly":[56],"splits":[57],"into":[62],"several":[63],"at":[65],"different":[66],"positions.":[67],"Further":[68],"on,":[69],"denoising":[71],"does":[72],"not":[73],"guarantee":[74],"any":[75],"optimality.":[76],"proposed":[78,115],"parametric":[79],"solves":[81],"all":[82],"these":[84],"problems.":[85],"Since":[86],"corresponding":[88],"log-likelihood":[89],"function":[90],"non-convex,":[92],"we":[93],"exploit":[94],"stochastic":[96],"method":[97],"particle":[99],"swarm":[100],"optimization":[101],"(PSO)":[102],"to":[103],"find":[104],"global":[106],"solution.":[107],"Simulation":[108],"results":[109],"confirm":[110],"effectiveness":[112],"approach,":[116],"reasonable":[119],"computational":[120],"cost.":[121]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
