{"id":"https://openalex.org/W4378195060","doi":"https://doi.org/10.1109/tgrs.2023.3279826","title":"CLEGAN: Toward Low-Light Image Enhancement for UAVs via Self-Similarity Exploitation","display_name":"CLEGAN: Toward Low-Light Image Enhancement for UAVs via Self-Similarity Exploitation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4378195060","doi":"https://doi.org/10.1109/tgrs.2023.3279826"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3279826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3279826","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","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/A5100727912","display_name":"Ling Xing","orcid":"https://orcid.org/0009-0001-6943-462X"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ling Xing","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101398852","display_name":"Hongyu Qu","orcid":"https://orcid.org/0000-0002-7631-8774"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyu Qu","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004345759","display_name":"Sheng Xu","orcid":"https://orcid.org/0000-0002-9017-1510"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Xu","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100387925","display_name":"Yao Tian","orcid":"https://orcid.org/0009-0002-3530-3944"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Tian","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100727912"],"corresponding_institution_ids":["https://openalex.org/I167027274"],"apc_list":null,"apc_paid":null,"fwci":2.8887,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.92628675,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7687535285949707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7178219556808472},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6112433075904846},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5776175260543823},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5604044198989868},{"id":"https://openalex.org/keywords/light-field","display_name":"Light field","score":0.5572314858436584},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5071368217468262},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5047076940536499},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38287487626075745},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.382673442363739},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35871627926826477}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7687535285949707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7178219556808472},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6112433075904846},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5776175260543823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5604044198989868},{"id":"https://openalex.org/C48983235","wikidata":"https://www.wikidata.org/wiki/Q593161","display_name":"Light field","level":2,"score":0.5572314858436584},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5071368217468262},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5047076940536499},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38287487626075745},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.382673442363739},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35871627926826477},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3279826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3279826","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G2427462704","display_name":null,"funder_award_id":"BK20200784","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G8047451824","display_name":null,"funder_award_id":"62102184","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G875527256","display_name":null,"funder_award_id":"2019M661852","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1580436348","https://openalex.org/W1976468890","https://openalex.org/W1982471090","https://openalex.org/W2054814429","https://openalex.org/W2076205488","https://openalex.org/W2116973876","https://openalex.org/W2127757833","https://openalex.org/W2146439264","https://openalex.org/W2150461190","https://openalex.org/W2150721269","https://openalex.org/W2154549868","https://openalex.org/W2254039850","https://openalex.org/W2412926690","https://openalex.org/W2465552163","https://openalex.org/W2566376500","https://openalex.org/W2611558102","https://openalex.org/W2767030724","https://openalex.org/W2783399029","https://openalex.org/W2791710889","https://openalex.org/W2804078698","https://openalex.org/W2842511635","https://openalex.org/W2946862972","https://openalex.org/W2949187370","https://openalex.org/W2955058313","https://openalex.org/W2962785568","https://openalex.org/W2963073614","https://openalex.org/W2981718299","https://openalex.org/W3005680577","https://openalex.org/W3034895805","https://openalex.org/W3035524453","https://openalex.org/W3035731588","https://openalex.org/W3048631361","https://openalex.org/W3100859887","https://openalex.org/W3102692100","https://openalex.org/W3108316907","https://openalex.org/W3117326213","https://openalex.org/W3120540810","https://openalex.org/W3121661546","https://openalex.org/W3146063680","https://openalex.org/W3177880808","https://openalex.org/W3179626733","https://openalex.org/W3185504516","https://openalex.org/W3198533054","https://openalex.org/W3199334903","https://openalex.org/W3205494421","https://openalex.org/W4210642697","https://openalex.org/W4285308363","https://openalex.org/W4290715747","https://openalex.org/W4292651731","https://openalex.org/W4293812270","https://openalex.org/W4294643358","https://openalex.org/W4294891478","https://openalex.org/W4297808394","https://openalex.org/W4312295033","https://openalex.org/W4312399981","https://openalex.org/W4312479775","https://openalex.org/W4312812783","https://openalex.org/W4312976992","https://openalex.org/W6746171365","https://openalex.org/W6752378368","https://openalex.org/W6754146604","https://openalex.org/W6763251565","https://openalex.org/W6774314701","https://openalex.org/W6779977557","https://openalex.org/W6798132488"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2965546495","https://openalex.org/W2354198838","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W1989130879","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2103419012"],"abstract_inverted_index":{"Low-light":[0],"remote":[1,39,179,211,254],"sensing":[2,40,180,212,255],"image":[3,33,256,261],"enhancement":[4],"(LLIE)":[5],"for":[6,152],"unmanned":[7],"aerial":[8],"vehicles":[9],"(UAVs)":[10],"has":[11],"significant":[12],"scientific":[13],"and":[14,52,90,173,195,225,258,274],"practical":[15,51],"value":[16],"because":[17],"unfavorable":[18],"lighting":[19],"conditions":[20],"make":[21,226],"capture":[22],"more":[23],"difficult,":[24],"resulting":[25],"in":[26,35,46,98,140,178,210,238],"undesired":[27],"images.":[28,181],"As":[29],"acquiring":[30],"real-world":[31],"low-light/normal-light":[32],"pairs":[34],"the":[36,56,60,70,72,85,114,124,128,132,144,149,156,160,168,205,233,239,269],"field":[37],"of":[38,112,171,185,208,219],"is":[41,50,66],"almost":[42],"infeasible,":[43],"performing":[44],"LLIE":[45,64,272],"an":[47,63],"unpaired":[48,81,125,271],"manner":[49],"valuable.":[53],"However,":[54],"without":[55],"paired":[57],"data":[58],"as":[59],"supervision,":[61],"learning":[62,96,115],"network":[65],"challenging.":[67],"To":[68],"address":[69],"challenges,":[71],"paper":[73],"proposes":[74],"a":[75,99,104,216],"novel":[76],"yet":[77],"effective":[78],"method":[79],"to":[80,122,166,214,242],"LLIE,":[82],"which":[83],"maximizes":[84],"mutual":[86],"information":[87,129,172,209,221],"between":[88],"low-light":[89,253,260],"restored":[91],"images":[92,213],"through":[93],"self-similarity":[94],"contrastive":[95],"(SSCL)":[97],"fully":[100],"unsupervised":[101],"fashion":[102],"within":[103],"single":[105,157],"deep":[106],"GAN":[107,158],"framework,":[108],"named":[109],"CLEGAN.":[110],"Instead":[111],"supervising":[113],"using":[116,127],"ground":[117],"truth":[118],"data,":[119],"we":[120],"propose":[121],"regularize":[123],"training":[126],"extracted":[130],"from":[131,148],"input":[133],"itself.":[134],"The":[135,247],"non-local":[136],"patch":[137],"sampling":[138],"strategy":[139],"SSCL":[141],"naturally":[142],"makes":[143],"negative":[145],"samples":[146,151],"differ":[147],"positive":[150],"discriminative":[153],"representation.":[154],"Moreover,":[155],"embeds":[159],"dual":[161],"illumination":[162,176],"perception":[163],"module":[164,193,200],"(DIPM)":[165],"handle":[167],"internal":[169,206],"recurrence":[170,207],"overall":[174],"uneven":[175],"distribution":[177],"DIPM":[182],"mainly":[183],"consists":[184],"two":[186],"cooperative":[187],"blocks:":[188],"spatial":[189],"adaptive":[190,197],"light":[191,198,228,245],"adjustment":[192,199],"(SALAM)":[194],"global":[196],"(GALAM).":[201],"Specifically,":[202],"SALAM":[203],"exploits":[204],"encode":[215],"wider":[217],"range":[218],"contextual":[220],"into":[222],"local":[223],"features":[224],"proper":[227],"estimation.":[229,246],"Simultaneously,":[230],"GALAM":[231],"enhances":[232],"most":[234],"valuable":[235],"illumination-related":[236],"channels":[237],"feature":[240],"map":[241],"achieve":[243],"better":[244],"experiments":[248],"on":[249],"several":[250,277],"datasets":[251,262],"including":[252],"dataset":[257],"public":[259],"show":[263],"that":[264],"CLEGAN":[265],"performs":[266],"favorably":[267],"against":[268],"existing":[270],"approaches,":[273],"even":[275],"outperforms":[276],"fully-supervised":[278],"methods.":[279]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}