{"id":"https://openalex.org/W2280704529","doi":"https://doi.org/10.1109/tit.2016.2636204","title":"Blind Recovery of Sparse Signals From Subsampled Convolution","display_name":"Blind Recovery of Sparse Signals From Subsampled Convolution","publication_year":2016,"publication_date":"2016-12-06","ids":{"openalex":"https://openalex.org/W2280704529","doi":"https://doi.org/10.1109/tit.2016.2636204","mag":"2280704529"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2016.2636204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2016.2636204","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1511.06149","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033729252","display_name":"Kiryung Lee","orcid":"https://orcid.org/0000-0003-1909-6041"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kiryung Lee","raw_affiliation_strings":["School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","<org_name>School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA</org_name>"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"<org_name>School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA</org_name>","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100616482","display_name":"Yanjun Li","orcid":"https://orcid.org/0000-0002-6640-1195"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjun Li","raw_affiliation_strings":["Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA","Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA#TAB#","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030843204","display_name":"Marius Junge","orcid":"https://orcid.org/0000-0002-5417-1636"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marius Junge","raw_affiliation_strings":["Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA","Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, Il., USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, Il., USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026523226","display_name":"Yoram Bresler","orcid":"https://orcid.org/0000-0002-9738-1094"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yoram Bresler","raw_affiliation_strings":["Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA","Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA#TAB#","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033729252"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":3.00847231,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.90594525,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"63","issue":"2","first_page":"802","last_page":"821"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9991999864578247,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.8511567115783691},{"id":"https://openalex.org/keywords/blind-deconvolution","display_name":"Blind deconvolution","score":0.8296791315078735},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7838754057884216},{"id":"https://openalex.org/keywords/identifiability","display_name":"Identifiability","score":0.7270068526268005},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6771475672721863},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5749425292015076},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5731868743896484},{"id":"https://openalex.org/keywords/prior-information","display_name":"Prior information","score":0.5305106043815613},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4962065815925598},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4792190194129944},{"id":"https://openalex.org/keywords/conic-section","display_name":"Conic section","score":0.418760746717453},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.411876916885376},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38111406564712524},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23958313465118408},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13080066442489624},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.10891503095626831}],"concepts":[{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.8511567115783691},{"id":"https://openalex.org/C30044814","wikidata":"https://www.wikidata.org/wiki/Q11334452","display_name":"Blind deconvolution","level":3,"score":0.8296791315078735},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7838754057884216},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.7270068526268005},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6771475672721863},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5749425292015076},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5731868743896484},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.5305106043815613},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4962065815925598},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4792190194129944},{"id":"https://openalex.org/C108598597","wikidata":"https://www.wikidata.org/wiki/Q124255","display_name":"Conic section","level":2,"score":0.418760746717453},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.411876916885376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38111406564712524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23958313465118408},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13080066442489624},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.10891503095626831},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tit.2016.2636204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2016.2636204","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1511.06149","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1511.06149","pdf_url":"https://arxiv.org/pdf/1511.06149","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2280704529","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1511.06149v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1511.06149","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1511.06149","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1511.06149","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1511.06149","pdf_url":"https://arxiv.org/pdf/1511.06149","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W589200591","https://openalex.org/W1526135953","https://openalex.org/W1567700673","https://openalex.org/W1600586145","https://openalex.org/W1600672635","https://openalex.org/W1953819449","https://openalex.org/W1977520307","https://openalex.org/W1995703890","https://openalex.org/W2014814922","https://openalex.org/W2015418199","https://openalex.org/W2027231794","https://openalex.org/W2030449718","https://openalex.org/W2046164006","https://openalex.org/W2054696696","https://openalex.org/W2110012675","https://openalex.org/W2129131372","https://openalex.org/W2134332047","https://openalex.org/W2140867429","https://openalex.org/W2159122269","https://openalex.org/W2160979406","https://openalex.org/W2162615905","https://openalex.org/W2169755932","https://openalex.org/W2176254067","https://openalex.org/W2187446163","https://openalex.org/W2289917018","https://openalex.org/W2323456480","https://openalex.org/W2472222212","https://openalex.org/W2951249062","https://openalex.org/W3104684837","https://openalex.org/W3209818445","https://openalex.org/W6631441237","https://openalex.org/W6633952271","https://openalex.org/W6676456659","https://openalex.org/W6685294672","https://openalex.org/W6696018292","https://openalex.org/W6720419206"],"related_works":["https://openalex.org/W2963805962","https://openalex.org/W2120564806","https://openalex.org/W2116680824","https://openalex.org/W1600586145","https://openalex.org/W1600672635","https://openalex.org/W2144730813","https://openalex.org/W2078397124","https://openalex.org/W2963972677","https://openalex.org/W2140867429","https://openalex.org/W2096575354","https://openalex.org/W1884015253","https://openalex.org/W3113425034","https://openalex.org/W2965497096","https://openalex.org/W2472222212","https://openalex.org/W2430435693","https://openalex.org/W2399402693","https://openalex.org/W2106005123","https://openalex.org/W2161764538","https://openalex.org/W221278985","https://openalex.org/W2007593159"],"abstract_inverted_index":{"Subsampled":[0],"blind":[1,122,155],"deconvolution":[2,123,156],"is":[3,112],"the":[4,17,68,72,92,97,121,140,165,169,174],"recovery":[5],"of":[6,12,19,47,50,139,168],"two":[7,62],"unknown":[8],"signals":[9],"from":[10],"samples":[11],"their":[13],"convolution.":[14],"To":[15],"overcome":[16],"ill-posedness":[18],"this":[20,143],"problem,":[21],"solutions":[22],"based":[23],"on":[24,71],"priors":[25],"tailored":[26],"to":[27,83,128],"specific":[28],"application":[29],"have":[30,40],"been":[31,105],"developed":[32],"in":[33,45,53,61,120,126,154],"practical":[34,78],"applications.":[35],"In":[36,101],"particular,":[37,102],"sparsity":[38,110],"models":[39],"provided":[41],"promising":[42],"priors.":[43],"However,":[44],"spite":[46],"empirical":[48,166],"success":[49],"these":[51],"methods":[52],"many":[54],"applications,":[55],"existing":[56],"analyses":[57],"are":[58],"rather":[59],"limited":[60],"main":[63],"ways:":[64],"by":[65,81,96],"disparity":[66],"between":[67],"theoretical":[69],"assumptions":[70],"signal":[73],"and/or":[74],"measurement":[75],"model":[76,111],"versus":[77],"setups;":[79],"or":[80],"failure":[82],"provide":[84,146],"a":[85,108,114,132],"performance":[86,153,167,175],"guarantee":[87],"for":[88,118],"parameter":[89],"values":[90],"within":[91],"optimal":[93,159],"regime":[94],"defined":[95],"information":[98],"theoretic":[99],"limits.":[100],"it":[103],"has":[104],"shown":[106],"that":[107,150],"naive":[109],"not":[113],"strong":[115],"enough":[116],"prior":[117],"identifiability":[119],"problem.":[124],"Instead,":[125],"addition":[127],"sparsity,":[129],"we":[130,145],"adopt":[131],"conic":[133],"constraint,":[134],"which":[135],"enforces":[136],"spectral":[137],"flatness":[138],"signals.":[141],"Under":[142],"prior,":[144],"an":[147],"iterative":[148,170],"algorithm":[149,171],"achieves":[151],"guaranteed":[152],"at":[157],"near":[158],"sample":[160],"complexity.":[161],"Numerical":[162],"results":[163],"show":[164],"agrees":[172],"with":[173],"guarantee.":[176]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
