{"id":"https://openalex.org/W4221138309","doi":"https://doi.org/10.1109/jstsp.2022.3170228","title":"A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems","display_name":"A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems","publication_year":2022,"publication_date":"2022-04-26","ids":{"openalex":"https://openalex.org/W4221138309","doi":"https://doi.org/10.1109/jstsp.2022.3170228"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2022.3170228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstsp.2022.3170228","pdf_url":"https://ieeexplore.ieee.org/ielx7/4200690/9829898/09763322.pdf","source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/4200690/9829898/09763322.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jakeoung Koo","orcid":"https://orcid.org/0000-0003-2614-3424"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Jakeoung Koo","raw_affiliation_strings":["School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K"],"raw_orcid":"https://orcid.org/0000-0003-2614-3424","affiliations":[{"raw_affiliation_string":"School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K","institution_ids":["https://openalex.org/I32062511"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Abderrahim Halimi","orcid":"https://orcid.org/0000-0002-8112-5352"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Abderrahim Halimi","raw_affiliation_strings":["School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K"],"raw_orcid":"https://orcid.org/0000-0002-8112-5352","affiliations":[{"raw_affiliation_string":"School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K","institution_ids":["https://openalex.org/I32062511"]}]},{"author_position":"last","author":{"id":null,"display_name":"Stephen McLaughlin","orcid":"https://orcid.org/0000-0002-9558-8294"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Stephen McLaughlin","raw_affiliation_strings":["School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K"],"raw_orcid":"https://orcid.org/0000-0002-9558-8294","affiliations":[{"raw_affiliation_string":"School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K","institution_ids":["https://openalex.org/I32062511"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32062511"],"apc_list":null,"apc_paid":null,"fwci":7.1591,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.96862847,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"16","issue":"4","first_page":"762","last_page":"774"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9772999882698059,"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"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9772999882698059,"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"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.0017999999690800905,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5435000061988831},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4677000045776367},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4659000039100647},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.45989999175071716},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4564000070095062},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4406000077724457},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.43970000743865967},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43939998745918274},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4032999873161316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7804999947547913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.551800012588501},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5435000061988831},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4900999963283539},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4406000077724457},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.43970000743865967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42340001463890076},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3296999931335449},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29330000281333923},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C128197687","wikidata":"https://www.wikidata.org/wiki/Q5477515","display_name":"Statistical noise","level":2,"score":0.25839999318122864}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstsp.2022.3170228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstsp.2022.3170228","pdf_url":"https://ieeexplore.ieee.org/ielx7/4200690/9829898/09763322.pdf","source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2201.10910","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.10910","pdf_url":"https://arxiv.org/pdf/2201.10910","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1109/jstsp.2022.3170228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstsp.2022.3170228","pdf_url":"https://ieeexplore.ieee.org/ielx7/4200690/9829898/09763322.pdf","source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G109462088","display_name":null,"funder_award_id":"EP/T00097X/1","funder_id":"https://openalex.org/F4320320005","funder_display_name":"Royal Academy of Engineering"},{"id":"https://openalex.org/G1927786901","display_name":null,"funder_award_id":"EP/S000631/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3436638565","display_name":"QuantIC - The UK Quantum Technology Hub in Quantum Imaging","funder_award_id":"EP/T00097X/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4098741818","display_name":null,"funder_award_id":"RF/201718/17128","funder_id":"https://openalex.org/F4320320005","funder_display_name":"Royal Academy of Engineering"},{"id":"https://openalex.org/G6034310695","display_name":null,"funder_award_id":"EP/S026428/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6338071468","display_name":"Signal Procssing in the Information Age","funder_award_id":"EP/S000631/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8434562786","display_name":null,"funder_award_id":"EP/T00097X","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8569792020","display_name":"Single Photons - Expanding the Spectrum (SPEXS)","funder_award_id":"EP/S026428/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8602226179","display_name":null,"funder_award_id":"EP/T00097X/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320320005","display_name":"Royal Academy of Engineering","ror":"https://ror.org/0526snb40"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221138309.pdf","grobid_xml":"https://content.openalex.org/works/W4221138309.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1513100184","https://openalex.org/W1852016712","https://openalex.org/W2086961060","https://openalex.org/W2087416986","https://openalex.org/W2100556411","https://openalex.org/W2125248163","https://openalex.org/W2130799745","https://openalex.org/W2133255058","https://openalex.org/W2145023731","https://openalex.org/W2547878593","https://openalex.org/W2562751859","https://openalex.org/W2613985060","https://openalex.org/W2809731038","https://openalex.org/W2884585870","https://openalex.org/W2890824287","https://openalex.org/W2898102229","https://openalex.org/W2963091558","https://openalex.org/W2963420686","https://openalex.org/W2963497304","https://openalex.org/W2964223102","https://openalex.org/W2966802213","https://openalex.org/W2983735215","https://openalex.org/W2987334222","https://openalex.org/W2994991102","https://openalex.org/W3018295689","https://openalex.org/W3018664053","https://openalex.org/W3030613902","https://openalex.org/W3035302306","https://openalex.org/W3038685279","https://openalex.org/W3100158650","https://openalex.org/W3109230621","https://openalex.org/W3133531783","https://openalex.org/W3133902371","https://openalex.org/W3160584984","https://openalex.org/W3163982590","https://openalex.org/W3181256344","https://openalex.org/W3184277843","https://openalex.org/W3198675548","https://openalex.org/W4244393449","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6640185926","https://openalex.org/W6677645113","https://openalex.org/W6729448088","https://openalex.org/W6729455701","https://openalex.org/W6730042731","https://openalex.org/W6730091202","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W3006943036","https://openalex.org/W4213225422","https://openalex.org/W2787767549","https://openalex.org/W3208423683","https://openalex.org/W2960005558","https://openalex.org/W3033523981","https://openalex.org/W3171617125","https://openalex.org/W4200511449","https://openalex.org/W2963058055","https://openalex.org/W2511279186"],"abstract_inverted_index":{"Deploying":[0],"3D":[1],"single-photon":[2,87],"Lidar":[3,88],"imaging":[4,13],"in":[5,14,66],"real":[6,164],"world":[7],"applications":[8],"presents":[9],"multiple":[10],"challenges":[11],"including":[12,157],"high":[15],"noise":[16,141],"environments.":[17],"Several":[18],"algorithms":[19],"have":[20],"been":[21],"proposed":[22,128],"to":[23,123,140,180],"address":[24],"these":[25],"issues":[26],"based":[27,113],"on":[28,161],"statistical":[29,73,110],"or":[30],"learning-based":[31,125],"frameworks.":[32],"Statistical":[33],"methods":[34,54],"provide":[35],"rich":[36],"information":[37,153],"about":[38,154],"the":[39,46,91,106,127,145,155,170,173],"inferred":[40],"parameters":[41],"but":[42,58],"are":[43,95],"limited":[44,59],"by":[45],"assumed":[47],"model":[48],"correlation":[49],"structures,":[50],"while":[51],"deep":[52,79],"learning":[53,80,112],"show":[55,166],"state-of-the-art":[56,181],"performance":[57],"inference":[60,174],"guarantees,":[61],"preventing":[62],"their":[63],"extended":[64],"use":[65],"critical":[67],"applications.":[68],"This":[69],"paper":[70],"unrolls":[71],"a":[72,77,131],"Bayesian":[74],"algorithm":[75,103],"into":[76,97],"new":[78],"architecture":[81,129],"for":[82],"robust":[83,139],"image":[84],"reconstruction":[85],"from":[86,105],"data,":[89],"i.e.":[90],"algorithm\u2019s":[92],"iterative":[93],"steps":[94],"converted":[96],"neural":[98],"network":[99,120],"layers.":[100],"The":[101],"resulting":[102],"benefits":[104],"advantages":[107],"of":[108,134,144,172],"both":[109],"and":[111,142,150,163,175],"frameworks,":[114],"providing":[115],"best":[116],"estimates":[117,156],"with":[118],"improved":[119],"interpretability.":[121],"Compared":[122],"existing":[124],"solutions,":[126],"requires":[130],"reduced":[132],"number":[133],"trainable":[135],"parameters,":[136],"is":[137],"more":[138],"mismodelling":[143],"system":[146],"impulse":[147],"response":[148],"function,":[149],"provides":[151],"richer":[152],"uncertainty":[158],"measures.":[159],"Results":[160],"synthetic":[162],"data":[165],"competitive":[167],"results":[168],"regarding":[169],"quality":[171],"computational":[176],"complexity":[177],"when":[178],"compared":[179],"algorithms.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-04-03T00:00:00"}