乐胖代购免代理版

{"id":"https://openalex.org/W4285058114","doi":"https://doi.org/10.1109/tci.2022.3183411","title":"Robust Deep Compressive Sensing With Recurrent-Residual Structural Constraints","display_name":"Robust Deep Compressive Sensing With Recurrent-Residual Structural Constraints","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285058114","doi":"https://doi.org/10.1109/tci.2022.3183411"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2022.3183411","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2573-0436"],"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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.07301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059518695","display_name":"Jun Niu","orcid":"https://orcid.org/0000-0002-1957-3234"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jun Niu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Duke University, Durham, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Duke University, Durham, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5059518695"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.214,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.371867,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":60,"max":70},"biblio":{"volume":"8","issue":null,"first_page":"551","last_page":"560"},"is_retracted":false,"is_paratext":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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9989,"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"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9983,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness","score":0.73799086}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.73799086},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7284428},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5564194},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.53821915},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5294489},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.52113795},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.5013571},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4910903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4423476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4139141},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3854447},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34708077},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28409085},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.086785495},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2022.3183411","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2573-0436"],"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":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2207.07301","pdf_url":"https://arxiv.org/pdf/2207.07301","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2207.07301","pdf_url":"https://arxiv.org/pdf/2207.07301","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.42}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":27,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W2018046189","https://openalex.org/W2084591647","https://openalex.org/W2104266187","https://openalex.org/W2110158442","https://openalex.org/W2119667497","https://openalex.org/W2121927366","https://openalex.org/W2159563318","https://openalex.org/W2161907179","https://openalex.org/W2331128040","https://openalex.org/W2590877996","https://openalex.org/W2612690371","https://openalex.org/W2784344583","https://openalex.org/W2798559986","https://openalex.org/W2915130236","https://openalex.org/W2944761405","https://openalex.org/W2962895431","https://openalex.org/W2963081547","https://openalex.org/W2963676935","https://openalex.org/W2980149757","https://openalex.org/W2981551308","https://openalex.org/W3009991223","https://openalex.org/W3048134800","https://openalex.org/W3090909748","https://openalex.org/W3103321200","https://openalex.org/W3115447952","https://openalex.org/W4234992901"],"related_works":["https://openalex.org/W4300044672","https://openalex.org/W4298287631","https://openalex.org/W4225394202","https://openalex.org/W3034302643","https://openalex.org/W3032952384","https://openalex.org/W2953061907","https://openalex.org/W2790400419","https://openalex.org/W2504004674","https://openalex.org/W2379589510","https://openalex.org/W2158224665"],"abstract_inverted_index":{"Existing":[0],"deep":[1,67,91,164],"compressive":[2],"sensing":[3,105],"(CS)":[4],"methods":[5],"either":[6],"ignore":[7],"adaptive":[8,72,104],"online":[9,73,82],"optimization":[10,83,128],"or":[11],"depend":[12],"on":[13],"costly":[14],"iterative":[15],"optimizer":[16],"during":[17],"reconstruction.":[18],"This":[19],"work":[20],"explores":[21],"a":[22,45],"novel":[23,46],"image":[24,59,115],"CS":[25,68,116,165],"framework":[26,69,151],"with":[27,84,156],"recurrent-residual":[28],"structural":[29],"constraint,":[30],"termed":[31],"as":[32],"

$\\mathrm{R}^{2}$

CS-NET.":[34],"The":[35,50,103,149],"notation=\"LaTeX\">$\\mathrm{R}^{2}$CS-NET":[37,77],"first":[38,66],"progressively":[39],"optimizes":[40],"the":[41,58,65,75,79,85,100,124,133,146],"acquired":[42],"samplings":[43],"through":[44,99],"recurrent":[47,126],"neural":[48],"network.":[49],"cascaded":[51],"residual":[52],"convolutional":[53],"network":[54,101],"then":[55],"fully":[56],"reconstructs":[57],"from":[60],"optimized":[61],"latent":[62,127],"representation.":[63],"As":[64],"efficiently":[70],"bridging":[71],"optimization,":[74],"integrates":[78],"robustness":[80,158],"of":[81,90],"efficiency":[86],"and":[87,159],"nonlinear":[88],"capacity":[89],"learning":[92],"methods.":[93],"Signal":[94],"correlation":[95],"has":[96],"been":[97],"addressed":[98],"architecture.":[102],"nature":[106],"further":[107],"makes":[108],"it":[109],"an":[110],"ideal":[111],"candidate":[112],"for":[113],"color":[114],"via":[117],"leveraging":[118],"channel":[119],"correlation.":[120],"Numerical":[121],"experiments":[122],"verify":[123],"proposed":[125],"design":[129],"not":[130],"only":[131],"fulfills":[132],"adaptation":[134],"motivation,":[135],"but":[136],"also":[137],"outperforms":[138],"classic":[139],"long":[140],"short-term":[141],"memory":[142],"(LSTM)":[143],"architecture":[144],"in":[145],"same":[147],"scenario.":[148],"overall":[150],"demonstrates":[152],"hardware":[153],"implementation":[154],"feasibility,":[155],"leading":[157],"generalization":[160],"capability":[161],"among":[162],"existing":[163],"benchmarks.":[166]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4285058114","counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2024-12-15T14:41:23.033678","created_date":"2022-07-13"}