{"id":"https://openalex.org/W4386597296","doi":"https://doi.org/10.1109/icip49359.2023.10223149","title":"Deep Learning Reconstruction for Single Pixel Imaging with Generative Adversarial Networks","display_name":"Deep Learning Reconstruction for Single Pixel Imaging with Generative Adversarial Networks","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386597296","doi":"https://doi.org/10.1109/icip49359.2023.10223149"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10223149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10223149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5112988744","display_name":"Baturalp G\u00fcven","orcid":null},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]},{"id":"https://openalex.org/I56303344","display_name":"Aselsan (Turkey)","ror":"https://ror.org/04knh8e66","country_code":"TR","type":"company","lineage":["https://openalex.org/I56303344"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Baturalp G\u00fcven","raw_affiliation_strings":["Bilkent University,Department of Electrical and Electronics Engineering,Ankara,Turkey","Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey","Aselsan Research Center, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bilkent University,Department of Electrical and Electronics Engineering,Ankara,Turkey","institution_ids":["https://openalex.org/I168864056"]},{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey","institution_ids":["https://openalex.org/I168864056"]},{"raw_affiliation_string":"Aselsan Research Center, Ankara, Turkey","institution_ids":["https://openalex.org/I56303344"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043745756","display_name":"Alper G\u00fcng\u00f6r","orcid":"https://orcid.org/0000-0002-3043-9124"},"institutions":[{"id":"https://openalex.org/I56303344","display_name":"Aselsan (Turkey)","ror":"https://ror.org/04knh8e66","country_code":"TR","type":"company","lineage":["https://openalex.org/I56303344"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Alper G\u00fcng\u00f6r","raw_affiliation_strings":["Aselsan Research Center,Ankara,Turkey","Aselsan Research Center, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aselsan Research Center,Ankara,Turkey","institution_ids":["https://openalex.org/I56303344"]},{"raw_affiliation_string":"Aselsan Research Center, Ankara, Turkey","institution_ids":["https://openalex.org/I56303344"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023062870","display_name":"M. Umut Bah\u00e7eci","orcid":null},"institutions":[{"id":"https://openalex.org/I56303344","display_name":"Aselsan (Turkey)","ror":"https://ror.org/04knh8e66","country_code":"TR","type":"company","lineage":["https://openalex.org/I56303344"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"M. Umut Bah\u00e7eci","raw_affiliation_strings":["Aselsan Research Center,Ankara,Turkey","Aselsan Research Center, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aselsan Research Center,Ankara,Turkey","institution_ids":["https://openalex.org/I56303344"]},{"raw_affiliation_string":"Aselsan Research Center, Ankara, Turkey","institution_ids":["https://openalex.org/I56303344"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032375235","display_name":"Tolga \u00c7ukur","orcid":"https://orcid.org/0000-0002-2296-851X"},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Tolga \u00c7ukur","raw_affiliation_strings":["Bilkent University,Department of Electrical and Electronics Engineering,Ankara,Turkey","Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bilkent University,Department of Electrical and Electronics Engineering,Ankara,Turkey","institution_ids":["https://openalex.org/I168864056"]},{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey","institution_ids":["https://openalex.org/I168864056"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2060","last_page":"2064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11996","display_name":"Random lasers and scattering media","score":1.0,"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":1.0,"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/T11897","display_name":"Digital Holography and Microscopy","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"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":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7618182301521301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7508277893066406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7477902173995972},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6919339895248413},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5947875380516052},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5516963601112366},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.49193263053894043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4894808530807495},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.48762789368629456},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.4582824110984802},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4245939552783966},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.11767041683197021}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7618182301521301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7508277893066406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7477902173995972},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6919339895248413},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5947875380516052},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5516963601112366},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.49193263053894043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4894808530807495},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.48762789368629456},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4582824110984802},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4245939552783966},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.11767041683197021},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10223149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10223149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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":27,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W1908963207","https://openalex.org/W2122548617","https://openalex.org/W2508457857","https://openalex.org/W2559655401","https://openalex.org/W2612690371","https://openalex.org/W2765431787","https://openalex.org/W2803747378","https://openalex.org/W2891138592","https://openalex.org/W2901589656","https://openalex.org/W2963073614","https://openalex.org/W2963768110","https://openalex.org/W2966687987","https://openalex.org/W2998280611","https://openalex.org/W3004261286","https://openalex.org/W3021094251","https://openalex.org/W3082973566","https://openalex.org/W3096831136","https://openalex.org/W3141117710","https://openalex.org/W4313198162","https://openalex.org/W4385453273","https://openalex.org/W6637568146","https://openalex.org/W6637940777","https://openalex.org/W6725080326","https://openalex.org/W6741832134","https://openalex.org/W6765779288","https://openalex.org/W6841337490"],"related_works":["https://openalex.org/W3217069185","https://openalex.org/W4308928038","https://openalex.org/W3049340819","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W2044092692","https://openalex.org/W2614621130","https://openalex.org/W4322709305","https://openalex.org/W2547665164","https://openalex.org/W2808862658"],"abstract_inverted_index":{"Single":[0],"pixel":[1],"imaging":[2,6],"(SPI)":[3],"enables":[4],"high-resolution":[5,25,102,125],"through":[7],"multiple":[8],"coded":[9,29,105],"measurements":[10,107],"based":[11,88],"on":[12,89],"low-resolution":[13,106],"snapshots.":[14],"An":[15],"inverse":[16],"problem":[17],"can":[18,65],"then":[19],"be":[20],"solved":[21],"to":[22,73,139,142,160],"reconstruct":[23],"a":[24,81,111,133],"image":[26,103],"given":[27],"the":[28,57,101,119,162],"measurements.":[30],"There":[31],"has":[32],"been":[33],"recent":[34],"interest":[35],"in":[36,42,67,172],"adoption":[37],"of":[38,113,174],"deep":[39,85],"neural":[40],"networks":[41],"SPI":[43,86,152],"reconstruction.":[44],"However,":[45],"existing":[46,151],"methods":[47,171],"are":[48,158],"commonly":[49],"trained":[50,130],"with":[51],"pixel-wise":[52,135],"loss":[53,138],"terms":[54,173],"such":[55],"as":[56],"\u2113":[58],"<inf":[59],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[60],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</inf>":[61],"-norm":[62],"loss,":[63],"which":[64],"result":[66],"spatial":[68],"blurring":[69],"and":[70,115,136,155,178],"poor":[71],"sensitivity":[72,141],"structural":[74,143],"details.":[75,144],"In":[76],"this":[77],"study,":[78],"we":[79],"propose":[80],"novel":[82],"approach":[83],"for":[84],"reconstruction":[87,153],"an":[90],"unrolled":[91],"conditional":[92],"generative":[93],"adversarial":[94,137],"network":[95],"(cGAN)":[96],"model.":[97],"The":[98,127,145,166],"generator":[99],"estimates":[100],"using":[104],"by":[108],"iterating":[109],"across":[110],"cascade":[112],"denoising":[114],"data-consistency":[116],"modules.":[117],"Meanwhile,":[118],"discriminator":[120],"distinguishes":[121],"real":[122],"versus":[123],"synthesized":[124],"images.":[126],"architecture":[128],"is":[129,148],"end-to-end":[131],"via":[132],"combined":[134],"enhance":[140],"proposed":[146,167],"method":[147,168],"demonstrated":[149],"against":[150],"methods,":[154],"ablation":[156],"studies":[157],"performed":[159],"demonstrate":[161],"individual":[163],"model":[164],"components.":[165],"outperforms":[169],"competing":[170],"both":[175],"quantitative":[176],"metrics":[177],"visual":[179],"quality.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}