{"id":"https://openalex.org/W7133217069","doi":"https://doi.org/10.1016/j.knosys.2026.115666","title":"Rethinking probabilistic learning for counterfactual low-light image enhancement in robust engineering vision systems","display_name":"Rethinking probabilistic learning for counterfactual low-light image enhancement in robust engineering vision systems","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133217069","doi":"https://doi.org/10.1016/j.knosys.2026.115666"},"language":"en","primary_location":{"id":"doi:10.1016/j.knosys.2026.115666","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.knosys.2026.115666","pdf_url":null,"source":{"id":"https://openalex.org/S10169007","display_name":"Knowledge-Based Systems","issn_l":"0950-7051","issn":["0950-7051","1872-7409"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge-Based Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.knosys.2026.115666","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zixiang Wei","orcid":"https://orcid.org/0009-0008-0844-8797"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]},{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zixiang Wei","raw_affiliation_strings":["School of Engineering and Materials Science (SEMS), Queen Mary University of London, London, E1 4NS, United Kingdom","Warwick Manufacturing Group, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom"],"raw_orcid":"https://orcid.org/0009-0008-0844-8797","affiliations":[{"raw_affiliation_string":"School of Engineering and Materials Science (SEMS), Queen Mary University of London, London, E1 4NS, United Kingdom","institution_ids":["https://openalex.org/I166337079"]},{"raw_affiliation_string":"Warwick Manufacturing Group, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127819517","display_name":"Yiting Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]},{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yiting Wang","raw_affiliation_strings":["School of Engineering and Materials Science (SEMS), Queen Mary University of London, London, E1 4NS, United Kingdom","Warwick Manufacturing Group, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-1268-0168","affiliations":[{"raw_affiliation_string":"School of Engineering and Materials Science (SEMS), Queen Mary University of London, London, E1 4NS, United Kingdom","institution_ids":["https://openalex.org/I166337079"]},{"raw_affiliation_string":"Warwick Manufacturing Group, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125482838","display_name":"Kurt Debattista","orcid":null},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kurt Debattista","raw_affiliation_strings":["Warwick Manufacturing Group, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-2982-5199","affiliations":[{"raw_affiliation_string":"Warwick Manufacturing Group, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127796038","display_name":"Valentina Donzella","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Valentina Donzella","raw_affiliation_strings":["School of Engineering and Materials Science (SEMS), Queen Mary University of London, London, E1 4NS, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-3408-6135","affiliations":[{"raw_affiliation_string":"School of Engineering and Materials Science (SEMS), Queen Mary University of London, London, E1 4NS, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5127796038"],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":{"value":3130,"currency":"USD","value_usd":3130},"apc_paid":{"value":3130,"currency":"USD","value_usd":3130},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20146512,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"340","issue":null,"first_page":"115666","last_page":"115666"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.5849999785423279,"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"}},"topics":[{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.5849999785423279,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.05640000104904175,"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/T13869","display_name":"Ocular and Laser Science Research","score":0.031199999153614044,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7178999781608582},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.6632000207901001},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6291999816894531},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.41530001163482666},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.3935000002384186},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3846000134944916},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.37439998984336853},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.3562999963760376},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.34549999237060547}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7178999781608582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6812000274658203},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.6632000207901001},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6291999816894531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5842999815940857},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46869999170303345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.423799991607666},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3562999963760376},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2635999917984009},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.knosys.2026.115666","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.knosys.2026.115666","pdf_url":null,"source":{"id":"https://openalex.org/S10169007","display_name":"Knowledge-Based Systems","issn_l":"0950-7051","issn":["0950-7051","1872-7409"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge-Based Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.knosys.2026.115666","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.knosys.2026.115666","pdf_url":null,"source":{"id":"https://openalex.org/S10169007","display_name":"Knowledge-Based Systems","issn_l":"0950-7051","issn":["0950-7051","1872-7409"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knowledge-Based Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4229739010334015,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335087","display_name":"Innovate UK","ror":"https://ror.org/05ar5fy68"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1580436348","https://openalex.org/W1987444808","https://openalex.org/W2011592399","https://openalex.org/W2016622085","https://openalex.org/W2054814429","https://openalex.org/W2074283154","https://openalex.org/W2076205488","https://openalex.org/W2102166818","https://openalex.org/W2116973876","https://openalex.org/W2130902307","https://openalex.org/W2133665775","https://openalex.org/W2145429839","https://openalex.org/W2150461190","https://openalex.org/W2565312867","https://openalex.org/W2566376500","https://openalex.org/W2754213847","https://openalex.org/W2905544033","https://openalex.org/W2992005611","https://openalex.org/W3101584909","https://openalex.org/W3119456559","https://openalex.org/W3119525307","https://openalex.org/W3120540810","https://openalex.org/W3121661546","https://openalex.org/W3155072588","https://openalex.org/W4226014430","https://openalex.org/W4285234777","https://openalex.org/W4285600750","https://openalex.org/W4366667835","https://openalex.org/W4387696030","https://openalex.org/W4391593953","https://openalex.org/W4394858294","https://openalex.org/W4399039280","https://openalex.org/W4399040945","https://openalex.org/W4401070625","https://openalex.org/W4405931317","https://openalex.org/W4407849538","https://openalex.org/W4409783504","https://openalex.org/W4411696341","https://openalex.org/W4412081915","https://openalex.org/W4414826014"],"related_works":[],"abstract_inverted_index":{"\u2022":[0,13,24],"1:":[1],"Formulates":[2],"low-light":[3,68],"enhancement":[4,70,145],"as":[5],"a":[6,10,16,120,156,186,203],"counterfactual":[7,140],"intervention":[8,151],"on":[9,32,212,230],"Retinex":[11],"SCM.":[12],"2:":[14],"Introduces":[15],"physics-guided":[17],"structural":[18],"causal":[19,107,131,158],"model":[20,159],"for":[21,90,138,239],"illumination\u2013reflectance":[22],"reasoning.":[23,141],"3:":[25],"Achieves":[26],"superior":[27],"perceptual":[28],"fidelity":[29],"and":[30,39,59,86,106,115,136,152,168,182,207,215,224,243],"robustness":[31],"multiple":[33],"benchmark":[34,234],"datasets.":[35],"Many":[36],"Architecture,":[37],"Engineering,":[38],"Construction":[40],"(AEC)":[41],"operations":[42],"must":[43],"operate":[44],"safely":[45],"under":[46],"visually":[47,88],"compromised":[48],"conditions,":[49,67],"dimensional":[50],"dimension,":[51],"including":[52],"night-time":[53],"navigation/driving,":[54],"tunnel":[55],"or":[56,61],"cave":[57],"exploration,":[58],"hazardous":[60],"confined":[62],"sites":[63],"observation.":[64],"In":[65],"such":[66],"image":[69,78,102,161],"is":[71],"expected":[72],"to":[73,82,112,194],"be":[74,87],"more":[75,240],"than":[76],"simply":[77],"brightening":[79],"but":[80],"also":[81],"maintain":[83],"mission-critical":[84],"structures":[85],"interpretable":[89],"deployment":[91],"in":[92,146,179,202],"safety-critical":[93],"conditions.":[94],"Nonetheless,":[95],"current":[96],"deep":[97],"LIE":[98,228],"techniques":[99],"conceptually":[100],"overlook":[101],"processing,":[103],"physical":[104,199],"priors":[105],"mechanisms":[108],"during":[109],"improvement,":[110],"contributing":[111],"constrained":[113],"ruggedness":[114],"generalisability.":[116],"This":[117],"paper":[118],"presents":[119],"Learning":[121],"Probabilistic":[122],"Low-light":[123],"Image":[124],"Enhancement":[125],"(LPIE)":[126],"network":[127],"that":[128,219],"explicitly":[129],"integrates":[130],"factors":[132],"into":[133,155],"the":[134,147,196,231,237],"LPIE":[135,142,220],"allows":[137],"probabilistic":[139,188],"casts":[143],"illumination":[144,171],"light":[148],"of":[149,160,198],"an":[150,174],"unrolls":[153],"it":[154],"structured":[157],"formation.":[162],"A":[163],"normalizing-flow":[164],"module":[165],"performs":[166],"invertible":[167],"physically":[169],"coherent":[170],"processes,":[172],"while":[173],"uncertainty-aware":[175],"Transformer":[176],"recovers":[177],"reflectance":[178],"spatially":[180],"varied":[181],"high-textured":[183],"regions.":[184],"Consequently,":[185],"complete":[187],"refinement":[189],"further":[190],"optimizes":[191],"all":[192],"elements":[193],"optimize":[195],"distribution":[197],"variables,":[200],"resulting":[201],"natural,":[204],"fine-detailed":[205],"photo":[206],"supporting":[208],"across-used":[209],"scenarios.":[210],"Experiments":[211],"public":[213],"benchmarks":[214],"AEC-relevant":[216],"datasets":[217],"show":[218],"achieves":[221],"state-of-the-art":[222],"performance":[223],"clearly":[225],"surpasses":[226],"recent":[227],"methods":[229],"widely":[232],"used":[233],"dataset,":[235],"paving":[236],"way":[238],"interpretable,":[241],"robust":[242],"trustworthy":[244],"automated":[245],"perception":[246],"systems.":[247]},"counts_by_year":[],"updated_date":"2026-03-08T06:56:09.383167","created_date":"2026-03-03T00:00:00"}