{"id":"https://openalex.org/W3004261286","doi":"https://doi.org/10.1109/globalsip45357.2019.8969255","title":"Learning Based Regularization for Spatial Multiplexing Cameras","display_name":"Learning Based Regularization for Spatial Multiplexing Cameras","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3004261286","doi":"https://doi.org/10.1109/globalsip45357.2019.8969255","mag":"3004261286"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip45357.2019.8969255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip45357.2019.8969255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5034589901","display_name":"O\u011fuzhan Fatih Kar","orcid":"https://orcid.org/0000-0002-5323-579X"},"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":"Oguzhan Fatih Kar","raw_affiliation_strings":["ASELSAN Research Center, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"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 Gungor","raw_affiliation_strings":["ASELSAN Research Center, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ASELSAN Research Center, Ankara, Turkey","institution_ids":["https://openalex.org/I56303344"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016552760","display_name":"H. Emre G\u00fcven","orcid":"https://orcid.org/0000-0001-5665-8782"},"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":"H. Emre Guven","raw_affiliation_strings":["ASELSAN Research Center, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ASELSAN Research Center, Ankara, Turkey","institution_ids":["https://openalex.org/I56303344"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I56303344"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9986000061035156,"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/regularization","display_name":"Regularization (linguistics)","score":0.7563735246658325},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7144196629524231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6340793967247009},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6295273303985596},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5952188968658447},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5913729667663574},{"id":"https://openalex.org/keywords/multiplexing","display_name":"Multiplexing","score":0.5613364577293396},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5062755346298218},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5018730163574219},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48275870084762573},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.089834064245224}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7563735246658325},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7144196629524231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6340793967247009},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6295273303985596},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5952188968658447},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5913729667663574},{"id":"https://openalex.org/C19275194","wikidata":"https://www.wikidata.org/wiki/Q222903","display_name":"Multiplexing","level":2,"score":0.5613364577293396},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5062755346298218},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5018730163574219},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48275870084762573},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.089834064245224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip45357.2019.8969255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip45357.2019.8969255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W140273596","https://openalex.org/W345598540","https://openalex.org/W764651262","https://openalex.org/W1908963207","https://openalex.org/W1963882359","https://openalex.org/W2005089986","https://openalex.org/W2035657841","https://openalex.org/W2041625018","https://openalex.org/W2056370875","https://openalex.org/W2087416986","https://openalex.org/W2104266187","https://openalex.org/W2123031198","https://openalex.org/W2124964692","https://openalex.org/W2146337213","https://openalex.org/W2164278908","https://openalex.org/W2508457857","https://openalex.org/W2803747378","https://openalex.org/W2891138592","https://openalex.org/W2913535645","https://openalex.org/W2949128855","https://openalex.org/W2963814976","https://openalex.org/W3102025760","https://openalex.org/W3106359998","https://openalex.org/W4244393449","https://openalex.org/W4292363360","https://openalex.org/W6678856934"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W2810730439","https://openalex.org/W4300044672","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W2378166785","https://openalex.org/W2896778670","https://openalex.org/W3209466624","https://openalex.org/W2765479697"],"abstract_inverted_index":{"In":[0],"this":[1,66,158],"paper,":[2],"we":[3,64,147,169],"consider":[4],"learning":[5,41,153],"based":[6,42,71,154],"regularization":[7,58,155,177],"for":[8,24,57,109,200,212],"compressive":[9,69],"sensing":[10,70],"reconstruction":[11,150,208],"using":[12,99,120,160],"focal":[13,86,128],"plane":[14,87,129],"array":[15],"sensors.":[16,91],"While":[17],"many":[18],"optimization":[19,61],"algorithms":[20],"employ":[21],"proximal":[22,172],"operators":[23],"regularization,":[25],"they":[26],"are":[27],"often":[28],"inadequate":[29],"in":[30,47,59,68,89,206,223],"fully":[31],"capturing":[32],"the":[33,52,80,134,171,176],"characteristics":[34],"of":[35,83,117,136,164,219,225],"complex":[36],"natural":[37],"images.":[38],"Recently,":[39],"deep":[40,181],"approaches":[43],"obtained":[44],"promising":[45],"results":[46,205,211],"different":[48,213],"imaging":[49,214],"problems,":[50],"creating":[51],"possibility":[53],"to":[54,156,175],"use":[55],"them":[56],"an":[60],"framework.":[62,166],"Here,":[63],"utilize":[65],"approach":[67],"spatial":[72,101,122],"multiplexing":[73,102],"cameras.":[74],"This":[75],"technique":[76],"is":[77],"motivated":[78],"by":[79,195],"high":[81,139,220],"cost":[82],"producing":[84],"large":[85],"arrays":[88],"infrared":[90],"Reconstruction":[92],"from":[93,142],"undersampled":[94,144],"measurements":[95],"can":[96],"be":[97],"done":[98],"a":[100,111,118,121,126,138,149,180,188],"camera":[103],"which":[104,204],"relies":[105],"on":[106],"multiple":[107,197],"snapshots":[108],"super-resolving":[110],"scene.":[112],"It":[113],"acquires":[114],"coded":[115],"projections":[116],"scene":[119],"light":[123],"modulator":[124],"and":[125,228],"low-resolution":[127],"array.":[130],"We":[131,185],"first":[132],"formulate":[133],"problem":[135,159],"finding":[137],"resolution":[140,221],"image":[141],"its":[143],"measurements.":[145],"Then,":[146],"develop":[148],"method":[151,163],"with":[152,179],"solve":[157],"alternating":[161],"direction":[162],"multipliers":[165],"For":[167],"this,":[168],"replace":[170],"operator":[173],"corresponding":[174],"function":[178],"convolutional":[182],"denoising":[183,191],"network.":[184],"also":[186],"enhance":[187],"previously":[189],"proposed":[190],"network's":[192],"training":[193,202],"phase":[194],"introducing":[196],"noise":[198,233],"realizations":[199],"each":[201],"patch,":[203],"better":[207],"performance.":[209],"Numerical":[210],"scenarios":[215],"show":[216],"successful":[217],"recovery":[218],"images":[222],"terms":[224],"PSNR,":[226],"SSIM":[227],"visual":[229],"quality":[230],"at":[231],"significant":[232],"levels.":[234]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
