{"id":"https://openalex.org/W4402352660","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650162","title":"Learned Measurement Interpolation for Scalable Compressive Sensing","display_name":"Learned Measurement Interpolation for Scalable Compressive Sensing","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352660","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650162"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650162","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5113373697","display_name":"Manato Shirai","orcid":null},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Manato Shirai","raw_affiliation_strings":["Hosei University,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Hosei University,Tokyo,Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051160626","display_name":"Fuma Kimishima","orcid":"https://orcid.org/0000-0003-4333-1929"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fuma Kimishima","raw_affiliation_strings":["Hosei University,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Hosei University,Tokyo,Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021863826","display_name":"Jinjia Zhou","orcid":"https://orcid.org/0000-0002-5078-0522"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinjia Zhou","raw_affiliation_strings":["Hosei University,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Hosei University,Tokyo,Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008003363","display_name":"Ryugo Morita","orcid":"https://orcid.org/0009-0007-6324-9291"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryugo Morita","raw_affiliation_strings":["Hosei University,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"Hosei University,Tokyo,Japan","institution_ids":["https://openalex.org/I204291657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5113373697"],"corresponding_institution_ids":["https://openalex.org/I204291657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15848097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9947999715805054,"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/interpolation","display_name":"Interpolation (computer graphics)","score":0.7314553260803223},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6435062885284424},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6028591990470886},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5153619647026062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2805561423301697},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07206705212593079}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.7314553260803223},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6435062885284424},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6028591990470886},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5153619647026062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2805561423301697},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07206705212593079},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650162","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2007674530","https://openalex.org/W2010981316","https://openalex.org/W2134809980","https://openalex.org/W2219727625","https://openalex.org/W2631883531","https://openalex.org/W2769654144","https://openalex.org/W2884144629","https://openalex.org/W2951751271","https://openalex.org/W2962895431","https://openalex.org/W2963081547","https://openalex.org/W2963764784","https://openalex.org/W2980149757","https://openalex.org/W3087291587","https://openalex.org/W3102015846","https://openalex.org/W3102722370","https://openalex.org/W3107269892","https://openalex.org/W4226232786","https://openalex.org/W4283806405","https://openalex.org/W4285253891","https://openalex.org/W4297697565"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W2810730439","https://openalex.org/W4300044672","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W2390279801","https://openalex.org/W2378166785"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"joint":[2],"compressive":[3,111],"sensing":[4,112],"image":[5,17,32,124],"acquisition":[6],"and":[7,19,31,46,83,119],"reconstruction":[8,68,113,120],"has":[9],"attracted":[10],"great":[11],"attention":[12],"in":[13,28],"the":[14,51,76,80,94,108,123],"field":[15],"of":[16,79],"compression":[18,35,90],"computational":[20],"sensors.":[21],"Although":[22],"these":[23],"methods":[24],"show":[25,100],"excellent":[26],"results":[27,99],"execution":[29],"speed":[30],"quality,":[33],"each":[34],"ratio":[36,91],"needs":[37],"one":[38],"specific":[39],"deep-learning":[40],"model":[41],"which":[42,74],"causes":[43],"training":[44],"costs":[45],"memory":[47],"pressure.":[48],"To":[49],"solve":[50],"above":[52],"problems,":[53],"we":[54],"propose":[55],"a":[56,66],"Measurements":[57],"Interpolation":[58],"strategy":[59],"(MI)":[60],"that":[61,101],"can":[62,104],"be":[63,105],"placed":[64],"before":[65],"DL-based":[67,110],"model.":[69],"MI":[70],"takes":[71],"original":[72,95],"measurements,":[73],"are":[75],"compressed":[77],"data":[78],"input":[81],"image,":[82],"outputs":[84],"interpolated":[85],"measurements":[86],"with":[87,107],"an":[88],"arbitrary":[89],"by":[92],"interpolating":[93],"measurements.":[96],"The":[97],"experimental":[98],"our":[102],"proposal":[103],"integrated":[106],"existing":[109],"works":[114],"to":[115],"achieve":[116],"scalable":[117],"sampling":[118],"while":[121],"keeping":[122],"quality.":[125]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}