{"id":"https://openalex.org/W7138069835","doi":"https://doi.org/10.1609/aaai.v40i12.37987","title":"Spectrally Adaptive Channel-aware Unrolling Network for Compressed Sensing","display_name":"Spectrally Adaptive Channel-aware Unrolling Network for Compressed Sensing","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138069835","doi":"https://doi.org/10.1609/aaai.v40i12.37987"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i12.37987","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i12.37987","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37987/41949","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37987/41949","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129643772","display_name":"Xiaoyang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiaoyang Wang","raw_affiliation_strings":["Northwest Polytechnical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwest Polytechnical University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129703657","display_name":"Hongping Gan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongping Gan","raw_affiliation_strings":["Northwest Polytechnical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwest Polytechnical University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5129643772"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24581006,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"12","first_page":"10190","last_page":"10198"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.963100016117096,"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":0.963100016117096,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.003599999938160181,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6377999782562256},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6169999837875366},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5722000002861023},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5196999907493591},{"id":"https://openalex.org/keywords/loop-unrolling","display_name":"Loop unrolling","score":0.4731000065803528},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.4675999879837036},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.42419999837875366},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4138000011444092},{"id":"https://openalex.org/keywords/conjugate-gradient-method","display_name":"Conjugate gradient method","score":0.39879998564720154},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.39649999141693115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577999830245972},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6377999782562256},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6169999837875366},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5722000002861023},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C76970557","wikidata":"https://www.wikidata.org/wiki/Q1869750","display_name":"Loop unrolling","level":3,"score":0.4731000065803528},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41510000824928284},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4138000011444092},{"id":"https://openalex.org/C81184566","wikidata":"https://www.wikidata.org/wiki/Q1191895","display_name":"Conjugate gradient method","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C149672232","wikidata":"https://www.wikidata.org/wiki/Q337048","display_name":"Adaptive optimization","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3447999954223633},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33169999718666077},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.27129998803138733},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C150452318","wikidata":"https://www.wikidata.org/wiki/Q4820432","display_name":"Augmented Lagrangian method","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C189693848","wikidata":"https://www.wikidata.org/wiki/Q6031064","display_name":"Information exchange","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.2515999972820282},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i12.37987","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i12.37987","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37987/41949","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/37987","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/37987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i12.37987","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i12.37987","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37987/41949","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6716188234","display_name":null,"funder_award_id":"62471395","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"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138069835.pdf","grobid_xml":"https://content.openalex.org/works/W7138069835.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"Unrolling":[1],"Networks":[2],"(DUNs)":[3],"integrate":[4],"classical":[5],"optimization":[6,68,170],"recovery":[7],"problems":[8],"in":[9,182],"Compressed":[10],"Sensing":[11],"(CS)":[12],"with":[13,117,168],"sophisticated":[14],"deep":[15],"learning":[16],"network":[17,60],"architectures,":[18],"leading":[19],"to":[20,87,104,145,159,173],"substantial":[21],"breakthroughs.":[22],"However,":[23],"prevailing":[24],"DUNs":[25],"generally":[26],"face":[27],"challenges":[28],"concerning":[29],"solidified":[30],"gradient":[31,67,96],"descent":[32],"step":[33,102],"size":[34,103],"strategies,":[35],"inadequate":[36],"feature":[37,152],"extraction":[38],"within":[39],"the":[40,63,90,112,169,175],"iterative":[41,48,165],"stage":[42],"and":[43,81,129,141,186],"limited":[44],"information":[45,148,161],"interaction":[46],"between":[47],"stages.":[49],"To":[50],"overcome":[51],"these":[52],"obstacles,":[53],"we":[54,72],"propose":[55],"SCU-Net,":[56],"a":[57,95],"channel-focused":[58],"unrolling":[59],"inspired":[61],"by":[62],"renowned":[64],"spectral":[65],"projected":[66],"algorithm.":[69],"In":[70],"particular,":[71],"tailore":[73],"two":[74],"pivotal":[75],"components,":[76],"Barzilai-Borwein-gradient":[77],"Descent":[78],"Optimizer":[79],"(BBDO)":[80],"Channel-guided":[82],"Cross-attention":[83],"Reconstruction":[84],"Module":[85],"(CCRM),":[86],"collaboratively":[88],"undertake":[89],"reconstruction":[91],"task.":[92],"BBDO":[93],"leverages":[94],"calculation":[97],"strategy":[98],"based":[99],"on":[100],"BB":[101],"enhance":[105],"data":[106],"fidelity":[107],"optimization,":[108],"while":[109],"CCRM":[110],"addresses":[111],"intricate":[113],"mapping":[114],"issue":[115],"associated":[116],"sparse":[118],"induction,":[119],"encompassing":[120],"customized":[121],"functionalities":[122],"from":[123],"Adaptive":[124],"Channel":[125],"Interaction":[126],"Layer":[127],"(ACIL)":[128],"Spatially":[130],"Augmented":[131],"Channel-aware":[132],"Unit":[133],"(SACU).":[134],"Among":[135],"them,":[136],"ACIL":[137],"amalgamates":[138],"convolution":[139],"operations":[140],"channel":[142],"attention":[143],"mechanisms":[144],"achieve":[146],"meticulous":[147],"screening":[149],"alongside":[150],"efficient":[151],"enhancement.":[153],"SACU":[154],"introduces":[155],"dual":[156],"reinforcement":[157],"variables":[158],"bolster":[160],"exchange":[162],"across":[163],"different":[164],"stages,":[166],"coupled":[167],"of":[171,177],"cross-attention":[172],"facilitate":[174],"modeling":[176],"long-distance":[178],"dependencies.":[179],"Extensive":[180],"experiments":[181],"both":[183],"image":[184],"CS":[185],"magnetic":[187],"resonance":[188],"imaging":[189],"exhibit":[190],"that":[191],"our":[192],"SCU-Net":[193],"manifests":[194],"superior":[195],"performance,":[196],"surpassing":[197],"state-of-the-art":[198],"methods.":[199]},"counts_by_year":[],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2026-03-18T00:00:00"}
