{"id":"https://openalex.org/W4221008746","doi":"https://doi.org/10.1109/icce53296.2022.9730511","title":"Multi-Exposure Image Fusion Using Cross-Attention Mechanism","display_name":"Multi-Exposure Image Fusion Using Cross-Attention Mechanism","publication_year":2022,"publication_date":"2022-01-07","ids":{"openalex":"https://openalex.org/W4221008746","doi":"https://doi.org/10.1109/icce53296.2022.9730511"},"language":"en","primary_location":{"id":"doi:10.1109/icce53296.2022.9730511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce53296.2022.9730511","pdf_url":null,"source":{"id":"https://openalex.org/S4363608007","display_name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-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/A5066092177","display_name":"Byung\u2010Nam Kim","orcid":"https://orcid.org/0000-0003-4302-462X"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]},{"id":"https://openalex.org/I2818286","display_name":"LG (United States)","ror":"https://ror.org/02b948n83","country_code":"US","type":"company","lineage":["https://openalex.org/I2818286","https://openalex.org/I4210131320"]}],"countries":["KR","US"],"is_corresponding":true,"raw_author_name":"Byungnam Kim","raw_affiliation_strings":["Yonsei University","LG Electronics"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"LG Electronics","institution_ids":["https://openalex.org/I2818286"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085334916","display_name":"Hyungjoo Jung","orcid":"https://orcid.org/0000-0003-0348-8649"},"institutions":[{"id":"https://openalex.org/I58716616","display_name":"Korea Institute of Science and Technology","ror":"https://ror.org/05kzfa883","country_code":"KR","type":"facility","lineage":["https://openalex.org/I27494661","https://openalex.org/I2801339556","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098","https://openalex.org/I4387152098","https://openalex.org/I58716616"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyungjoo Jung","raw_affiliation_strings":["Korea Institute of Science and Technology (KIST)"],"affiliations":[{"raw_affiliation_string":"Korea Institute of Science and Technology (KIST)","institution_ids":["https://openalex.org/I58716616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073320959","display_name":"Kwanghoon Sohn","orcid":"https://orcid.org/0000-0002-3715-0331"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwanghoon Sohn","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066092177"],"corresponding_institution_ids":["https://openalex.org/I193775966","https://openalex.org/I2818286"],"apc_list":null,"apc_paid":null,"fwci":0.3581,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.65756049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":1.0,"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":1.0,"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":0.9998999834060669,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7879420518875122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7434678673744202},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5739015936851501},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.55076664686203},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5319056510925293},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.4878380596637726},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4836255609989166},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.48246079683303833},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46961766481399536},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46518391370773315},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.45332637429237366},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44422969222068787},{"id":"https://openalex.org/keywords/dynamic-range","display_name":"Dynamic range","score":0.23544076085090637}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7879420518875122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7434678673744202},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5739015936851501},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.55076664686203},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5319056510925293},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.4878380596637726},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4836255609989166},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.48246079683303833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46961766481399536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46518391370773315},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.45332637429237366},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44422969222068787},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.23544076085090637},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","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/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/icce53296.2022.9730511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce53296.2022.9730511","pdf_url":null,"source":{"id":"https://openalex.org/S4363608007","display_name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","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":39,"referenced_works":["https://openalex.org/W1580436348","https://openalex.org/W1861492603","https://openalex.org/W1971693194","https://openalex.org/W1985972181","https://openalex.org/W1998393535","https://openalex.org/W2054273865","https://openalex.org/W2073623229","https://openalex.org/W2091484864","https://openalex.org/W2116702374","https://openalex.org/W2136001449","https://openalex.org/W2163932540","https://openalex.org/W2412926690","https://openalex.org/W2590560192","https://openalex.org/W2752782242","https://openalex.org/W2752849939","https://openalex.org/W2796014824","https://openalex.org/W2798987894","https://openalex.org/W2884068670","https://openalex.org/W2921083834","https://openalex.org/W2955058313","https://openalex.org/W2963091558","https://openalex.org/W2963530785","https://openalex.org/W3000491333","https://openalex.org/W3014859219","https://openalex.org/W3014896127","https://openalex.org/W3034972231","https://openalex.org/W3042980047","https://openalex.org/W3105639468","https://openalex.org/W4288358095","https://openalex.org/W4295312788","https://openalex.org/W4302275239","https://openalex.org/W4385245566","https://openalex.org/W6639102338","https://openalex.org/W6739901393","https://openalex.org/W6746034047","https://openalex.org/W6762746313","https://openalex.org/W6766978945","https://openalex.org/W6769047827","https://openalex.org/W6795300077"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W3155418658","https://openalex.org/W4243199227","https://openalex.org/W2379948177","https://openalex.org/W2582651754"],"abstract_inverted_index":{"Multi-exposure":[0],"fusion":[1,106,129],"(MEF)":[2],"is":[3,143],"a":[4,51,60,99,122,128,131,135,146],"popular":[5],"method":[6,85,159],"for":[7,32,63,107],"obtaining":[8],"high":[9],"dynamic":[10,17],"range":[11,18],"(HDR)":[12],"image":[13,65,142],"from":[14,68],"multiple":[15],"low":[16],"(LDR)":[19],"images.":[20],"Even":[21],"though":[22],"recent":[23],"works":[24],"have":[25],"employed":[26],"the":[27,34,74,77,81,104,109,115,140,154,157,166],"convolutional":[28],"neural":[29],"networks":[30],"(CNNs)":[31],"solving":[33],"MEF":[35],"problem,":[36],"there":[37],"still":[38],"remain":[39],"various":[40],"challenges,":[41],"such":[42],"as":[43,103],"color":[44,175],"distortion":[45],"and":[46,112,134,139,168,177],"detail":[47,100,136],"loss,":[48],"due":[49],"to":[50],"limited":[52],"receptive":[53],"field.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58,97],"present":[59],"cross-attention":[61,132],"module":[62,102,133],"multi-exposure":[64],"fusion.":[66],"Different":[67],"existing":[69],"CNN-based":[70],"methods":[71],"that":[72,151],"capture":[73],"contexts":[75],"of":[76,130,174],"local":[78,88],"region":[79],"in":[80,114,164,172],"target":[82],"image,":[83],"our":[84],"adaptively":[86],"aggregates":[87],"features":[89],"with":[90,125,153],"global":[91],"dependencies":[92],"at":[93],"all":[94],"positions.":[95],"Furthermore,":[96],"propose":[98],"compensation":[101,137],"feature":[105,123],"restoring":[108],"loss":[110],"(color":[111],"detail)":[113],"saturation":[116],"region.":[117],"Our":[118],"proposed":[119,158],"network":[120],"performs":[121],"extraction":[124],"an":[126],"encoder,":[127],"module,":[138],"fused":[141],"reconstructed":[144],"by":[145],"decoder.":[147],"Experimental":[148],"results":[149],"show":[150],"compared":[152],"state-of-the-art":[155],"methods,":[156],"can":[160],"obtain":[161],"better":[162],"performance":[163],"both":[165],"subjective":[167],"objective":[169],"evaluation,":[170],"particularly":[171],"terms":[173],"expression":[176],"detail-preserving.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-10T06:02:16.177343","created_date":"2025-10-10T00:00:00"}