{"id":"https://openalex.org/W3209912471","doi":"https://doi.org/10.1109/tsp.2021.3133710","title":"\u201cSelf-Wiener\u201d Filtering: Data-Driven Deconvolution of Deterministic Signals","display_name":"\u201cSelf-Wiener\u201d Filtering: Data-Driven Deconvolution of Deterministic Signals","publication_year":2021,"publication_date":"2021-12-13","ids":{"openalex":"https://openalex.org/W3209912471","doi":"https://doi.org/10.1109/tsp.2021.3133710","mag":"3209912471"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2021.3133710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2021.3133710","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"},"type":"article","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/A5081331925","display_name":"Amir Weiss","orcid":"https://orcid.org/0000-0002-3104-6939"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Amir Weiss","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034942163","display_name":"Boaz Nadler","orcid":"https://orcid.org/0000-0002-9777-4576"},"institutions":[{"id":"https://openalex.org/I53964585","display_name":"Weizmann Institute of Science","ror":"https://ror.org/0316ej306","country_code":"IL","type":"education","lineage":["https://openalex.org/I53964585"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Boaz Nadler","raw_affiliation_strings":["Department of Computer Science and Applied Mathematics, Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Applied Mathematics, Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel","institution_ids":["https://openalex.org/I53964585"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5081331925"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":0.7087,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66711286,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"70","issue":null,"first_page":"468","last_page":"481"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.7741694450378418},{"id":"https://openalex.org/keywords/bandlimiting","display_name":"Bandlimiting","score":0.7720068693161011},{"id":"https://openalex.org/keywords/wiener-filter","display_name":"Wiener filter","score":0.7323062419891357},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.699485182762146},{"id":"https://openalex.org/keywords/wiener-deconvolution","display_name":"Wiener deconvolution","score":0.638432502746582},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6240069270133972},{"id":"https://openalex.org/keywords/blind-deconvolution","display_name":"Blind deconvolution","score":0.5885547399520874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.578935980796814},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.569815456867218},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.551655113697052},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.504341721534729},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5016295909881592},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.49686458706855774},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.4678208529949188},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.353044331073761},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.21930962800979614},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19973251223564148},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14172813296318054}],"concepts":[{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.7741694450378418},{"id":"https://openalex.org/C129997835","wikidata":"https://www.wikidata.org/wiki/Q806263","display_name":"Bandlimiting","level":3,"score":0.7720068693161011},{"id":"https://openalex.org/C18537770","wikidata":"https://www.wikidata.org/wiki/Q25523","display_name":"Wiener filter","level":2,"score":0.7323062419891357},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.699485182762146},{"id":"https://openalex.org/C15652857","wikidata":"https://www.wikidata.org/wiki/Q599016","display_name":"Wiener deconvolution","level":4,"score":0.638432502746582},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6240069270133972},{"id":"https://openalex.org/C30044814","wikidata":"https://www.wikidata.org/wiki/Q11334452","display_name":"Blind deconvolution","level":3,"score":0.5885547399520874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.578935980796814},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.569815456867218},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.551655113697052},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.504341721534729},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5016295909881592},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.49686458706855774},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.4678208529949188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.353044331073761},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.21930962800979614},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19973251223564148},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14172813296318054},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2021.3133710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2021.3133710","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W5737729","https://openalex.org/W191129667","https://openalex.org/W210359992","https://openalex.org/W1510355813","https://openalex.org/W1517926209","https://openalex.org/W1536252645","https://openalex.org/W1604736418","https://openalex.org/W1606782322","https://openalex.org/W1766888123","https://openalex.org/W1989898472","https://openalex.org/W2024605830","https://openalex.org/W2034584585","https://openalex.org/W2040975718","https://openalex.org/W2042359472","https://openalex.org/W2046090497","https://openalex.org/W2058278486","https://openalex.org/W2064421023","https://openalex.org/W2072196484","https://openalex.org/W2074682976","https://openalex.org/W2079724595","https://openalex.org/W2088349114","https://openalex.org/W2088909704","https://openalex.org/W2106809525","https://openalex.org/W2106834319","https://openalex.org/W2113316428","https://openalex.org/W2113920449","https://openalex.org/W2115451584","https://openalex.org/W2126870831","https://openalex.org/W2131408147","https://openalex.org/W2132105090","https://openalex.org/W2135046866","https://openalex.org/W2141116650","https://openalex.org/W2145437669","https://openalex.org/W2146842127","https://openalex.org/W2147298660","https://openalex.org/W2148688551","https://openalex.org/W2149846618","https://openalex.org/W2152210587","https://openalex.org/W2158208985","https://openalex.org/W2164385155","https://openalex.org/W2434806141","https://openalex.org/W2493200156","https://openalex.org/W2559035186","https://openalex.org/W2801179766","https://openalex.org/W2913465992","https://openalex.org/W2951920181","https://openalex.org/W3147919432","https://openalex.org/W3194813201","https://openalex.org/W4206196235","https://openalex.org/W4229530126","https://openalex.org/W4239378390","https://openalex.org/W4240691156","https://openalex.org/W6600233301","https://openalex.org/W6636103335","https://openalex.org/W6681807782"],"related_works":["https://openalex.org/W2363395155","https://openalex.org/W1986055485","https://openalex.org/W2276822462","https://openalex.org/W4247791375","https://openalex.org/W1981527759","https://openalex.org/W1529631489","https://openalex.org/W2131048104","https://openalex.org/W2591816639","https://openalex.org/W2094499330","https://openalex.org/W2316473979"],"abstract_inverted_index":{"We":[0,26,78,106],"consider":[1],"the":[2,9,38,45,53,67,110,122,128,163],"problem":[3],"of":[4,11,83,155],"robust":[5],"deconvolution,":[6],"and":[7,21,87,100,120,150],"particularly":[8],"recovery":[10],"an":[12],"unknown":[13,54],"deterministic":[14,55],"signal":[15,56],"convolved":[16],"with":[17],"a":[18,28,62,75,80,152],"known":[19],"filter":[20,50],"corrupted":[22],"by":[23],"additive":[24],"noise.":[25],"present":[27],"novel,":[29],"non-iterative":[30],"data-driven":[31,76],"approach.":[32],"Specifically,":[33],"our":[34,84,114,138],"algorithm":[35],"works":[36],"in":[37,74,109,121,136,162],"frequency-domain,":[39],"where":[40,66],"it":[41,126],"tries":[42],"to":[43,61,159],"mimic":[44],"optimal":[46,129],"unrealizable":[47,130],"non-linear":[48],"Wiener-like":[49],"as":[51,133],"if":[52],"were":[57],"known.":[58],"This":[59],"leads":[60],"threshold-type":[63],"regularized":[64],"estimator,":[65,86],"threshold":[68],"at":[69,97],"each":[70],"frequency":[71],"is":[72,140],"determined":[73],"manner.":[77],"perform":[79],"theoretical":[81],"analysis":[82],"proposed":[85],"derive":[88],"approximate":[89],"formulas":[90],"for":[91,143],"its":[92],"Mean":[93],"Squared":[94],"Error":[95],"(MSE)":[96],"both":[98],"low":[99,111],"high":[101,123],"Signal-to-Noise":[102],"Ratio":[103],"(SNR)":[104],"regimes.":[105],"show":[107],"that":[108],"SNR":[112,124],"regime":[113,125],"method":[115],"provides":[116,151],"enhanced":[117],"noise":[118],"suppression,":[119],"approaches":[127],"solution.":[131],"Further,":[132],"we":[134],"demonstrate":[135],"simulations,":[137],"solution":[139],"highly":[141],"suitable":[142],"(approximately)":[144],"bandlimited":[145],"or":[146],"frequency-domain":[147],"sparse":[148],"signals,":[149],"significant":[153],"gain":[154],"several":[156],"dBs":[157],"relative":[158],"other":[160],"methods":[161],"resulting":[164],"MSE.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-10T00:00:00"}