{"id":"https://openalex.org/W4293518741","doi":"https://doi.org/10.1109/icme52920.2022.9859640","title":"Deep Unfolding for Compressed Sensing with Denoiser","display_name":"Deep Unfolding for Compressed Sensing with Denoiser","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4293518741","doi":"https://doi.org/10.1109/icme52920.2022.9859640"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859640","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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/A5044883250","display_name":"Chi Ma","orcid":"https://orcid.org/0000-0001-6774-8935"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chi Ma","raw_affiliation_strings":["Shenzhen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045125183","display_name":"Joey Tianyi Zhou","orcid":"https://orcid.org/0000-0002-4675-7055"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Joey Tianyi Zhou","raw_affiliation_strings":["A*STAR Centre for Frontier AI Research (CFAR)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"A*STAR Centre for Frontier AI Research (CFAR)","institution_ids":["https://openalex.org/I115228651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100696673","display_name":"Xiao Zhang","orcid":"https://orcid.org/0000-0002-8020-6142"},"institutions":[{"id":"https://openalex.org/I174442536","display_name":"South Central Minzu University","ror":"https://ror.org/03d7sax13","country_code":"CN","type":"education","lineage":["https://openalex.org/I174442536"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Zhang","raw_affiliation_strings":["South-Central University for Nationalities"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South-Central University for Nationalities","institution_ids":["https://openalex.org/I174442536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048421704","display_name":"Yu Zhou","orcid":"https://orcid.org/0000-0002-3224-0063"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhou","raw_affiliation_strings":["Shenzhen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2177,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87841626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"06"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9994000196456909,"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/interpretability","display_name":"Interpretability","score":0.8749198317527771},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7168901562690735},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.684799075126648},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6157112717628479},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5901339054107666},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.554380476474762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5394855737686157},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5055553913116455},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4944493770599365},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.476161926984787},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.4621959328651428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4567405581474304},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.35926997661590576},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3444591760635376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3365686535835266}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8749198317527771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7168901562690735},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.684799075126648},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6157112717628479},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5901339054107666},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.554380476474762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5394855737686157},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5055553913116455},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4944493770599365},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.476161926984787},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.4621959328651428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4567405581474304},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35926997661590576},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3444591760635376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3365686535835266}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme52920.2022.9859640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859640","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G3138428343","display_name":null,"funder_award_id":"JCYJ2020010911041013","funder_id":"https://openalex.org/F4320329791","funder_display_name":"Shenzhen Fundamental Research Program"},{"id":"https://openalex.org/G7012120421","display_name":null,"funder_award_id":"61702336,61902437","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329791","display_name":"Shenzhen Fundamental Research Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1930824406","https://openalex.org/W2018990310","https://openalex.org/W2028349405","https://openalex.org/W2045737896","https://openalex.org/W2047920195","https://openalex.org/W2121927366","https://openalex.org/W2171783193","https://openalex.org/W2273561594","https://openalex.org/W2408480187","https://openalex.org/W2410988050","https://openalex.org/W2476548250","https://openalex.org/W2500374603","https://openalex.org/W2543013670","https://openalex.org/W2573726823","https://openalex.org/W2590877996","https://openalex.org/W2739757502","https://openalex.org/W2798559986","https://openalex.org/W2805167149","https://openalex.org/W2963081547","https://openalex.org/W3018622322","https://openalex.org/W3087291587","https://openalex.org/W3115447952","https://openalex.org/W3188921881","https://openalex.org/W4250955649","https://openalex.org/W4288409091","https://openalex.org/W4297979842","https://openalex.org/W6600284362","https://openalex.org/W6753088188","https://openalex.org/W6760820039"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4319993887","https://openalex.org/W4297789176","https://openalex.org/W2896778670","https://openalex.org/W2768346313","https://openalex.org/W2963249138","https://openalex.org/W2998594699","https://openalex.org/W4396882122","https://openalex.org/W3178016723","https://openalex.org/W2968060152"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"witnessed":[3],"increasingly":[4],"more":[5,36,143],"exercises":[6],"and":[7,24,145],"uses":[8],"of":[9,35,48,58,120,131],"deep":[10],"unfolding":[11],"network":[12],"(DUN)":[13],"in":[14,65],"image":[15,59,79,118,128],"compressed":[16],"sensing":[17],"(CS)":[18],"due":[19],"to":[20,103,116],"its":[21],"high":[22],"performance":[23],"interpretability.":[25],"However,":[26],"the":[27,41,46,49,55,66,93,105,110,117,121,127,132,139],"existing":[28,67],"DUN":[29,77],"does":[30],"not":[31],"make":[32],"full":[33],"use":[34],"flexible":[37],"regularization":[38,82],"methods.":[39,68],"Besides,":[40],"intermediate":[42],"information":[43,100,107,113],"generated":[44,108],"during":[45,109],"iterations":[47],"DUN,":[50],"which":[51,85,124],"is":[52,114,142],"crucial":[53],"for":[54,78],"quality":[56],"improvement":[57],"reconstruction,":[60],"has":[61],"been":[62],"largely":[63],"overlooked":[64],"To":[69],"alleviate":[70],"this":[71],"problem,":[72],"we":[73,97],"propose":[74],"a":[75],"novel":[76],"CS":[80],"with":[81],"by":[83],"denoising":[84],"casts":[86],"half":[87],"quadratic":[88],"splitting":[89],"(HQS)":[90],"algorithm":[91],"into":[92],"neural":[94],"network.":[95],"Further,":[96],"design":[98],"an":[99],"collection":[101],"strategy":[102],"leverage":[104],"useful":[106],"iterations.":[111],"The":[112,134],"provided":[115],"denoiser":[119],"proposed":[122,140],"network,":[123],"could":[125],"enhance":[126],"processing":[129],"ability":[130],"denoiser.":[133],"extensive":[135],"experiments":[136],"demonstrate":[137],"that":[138],"method":[141],"efficient":[144],"achieves":[146],"state-of-the-art":[147],"reconstruction":[148],"quality.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}