{"id":"https://openalex.org/W7147056244","doi":"https://doi.org/10.48550/arxiv.2603.29732","title":"Compressive sensing inspired self-supervised single-pixel imaging","display_name":"Compressive sensing inspired self-supervised single-pixel imaging","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147056244","doi":"https://doi.org/10.48550/arxiv.2603.29732"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29732","pdf_url":null,"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":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.29732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132568043","display_name":"Jijun Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Jijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132642578","display_name":"Yifan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068942203","display_name":"Libang Chen","orcid":"https://orcid.org/0000-0003-1222-2914"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Libang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132713375","display_name":"Yiqiang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132698967","display_name":"Ye Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Ye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132682910","display_name":"Mingliang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Mingliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132662552","display_name":"Zhe Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132643080","display_name":"Xuelong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xuelong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11996","display_name":"Random lasers and scattering media","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11996","display_name":"Random lasers and scattering media","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.014999999664723873,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.0026000000070780516,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7159000039100647},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6402000188827515},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.6168000102043152},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5709999799728394},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4239000082015991},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4106999933719635},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40220001339912415},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.39660000801086426},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.3953000009059906}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7159000039100647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6402000188827515},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6402000188827515},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.6168000102043152},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5709999799728394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5564000010490417},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4681999981403351},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4106999933719635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4083000123500824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.3953000009059906},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.30300000309944153},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.29580000042915344},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.26989999413490295},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.26330000162124634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29732","pdf_url":null,"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":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29732","pdf_url":null,"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":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5108591914176941,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Single-pixel":[0],"imaging":[1,6],"(SPI)":[2],"is":[3],"a":[4,48,69,74,82,113,161],"promising":[5],"modality":[7],"with":[8,112],"distinctive":[9],"advantages":[10],"in":[11,122,154],"strongly":[12],"perturbed":[13],"environments.":[14],"Existing":[15],"SPI":[16],"methods":[17,150],"lack":[18],"physical":[19,120],"sparsity":[20,121],"constraints":[21],"and":[22,28,38,73,93,100,129],"overlook":[23],"the":[24,58,123],"integration":[25],"of":[26,68],"local":[27,92],"global":[29,94],"features,":[30],"leading":[31],"to":[32,90,117,131],"severe":[33],"noise":[34,127],"vulnerability,":[35],"structural":[36],"distortions":[37],"blurred":[39],"details.":[40],"To":[41],"address":[42],"these":[43],"limitations,":[44],"we":[45],"propose":[46],"SISTA-Net,":[47],"compressive":[49],"sensing-inspired":[50],"self-supervised":[51],"method":[52],"for":[53],"single-pixel":[54],"imaging.":[55],"SISTA-Net":[56,147],"unfolds":[57],"Iterative":[59],"Shrinkage-Thresholding":[60],"Algorithm":[61],"(ISTA)":[62],"into":[63],"an":[64],"interpretable":[65],"network":[66],"consisting":[67],"data":[70],"fidelity":[71,79],"module":[72,80],"proximal":[75],"mapping":[76],"module.":[77],"The":[78],"adopts":[81],"hybrid":[83],"CNN-Visual":[84],"State":[85],"Space":[86],"Model":[87],"(VSSM)":[88],"architecture":[89],"integrate":[91],"feature":[95],"modeling,":[96],"enhancing":[97],"reconstruction":[98],"integrity":[99],"fidelity.":[101],"We":[102],"leverage":[103],"deep":[104],"nonlinear":[105],"networks":[106],"as":[107],"adaptive":[108],"sparse":[109],"transforms":[110],"combined":[111],"learnable":[114],"soft-thresholding":[115],"operator":[116],"impose":[118],"explicit":[119],"latent":[124],"domain,":[125],"enabling":[126],"suppression":[128],"robustness":[130],"interference":[132],"even":[133],"at":[134],"extremely":[135],"low":[136],"sampling":[137],"rates.":[138],"Extensive":[139],"experiments":[140],"on":[141],"multiple":[142],"simulation":[143],"scenarios":[144],"demonstrate":[145],"that":[146],"outperforms":[148],"state-of-the-art":[149],"by":[151],"2.6":[152],"dB":[153,163],"PSNR.":[155],"Real-world":[156],"far-field":[157],"underwater":[158],"tests":[159],"yield":[160],"3.4":[162],"average":[164],"PSNR":[165],"improvement,":[166],"validating":[167],"its":[168],"robust":[169],"anti-interference":[170],"capability.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}