{"id":"https://openalex.org/W4385478725","doi":"https://doi.org/10.1007/s40747-023-01185-2","title":"Multi-scale attention-based lightweight network with dilated convolutions for infrared and visible image fusion","display_name":"Multi-scale attention-based lightweight network with dilated convolutions for infrared and visible image fusion","publication_year":2023,"publication_date":"2023-08-03","ids":{"openalex":"https://openalex.org/W4385478725","doi":"https://doi.org/10.1007/s40747-023-01185-2"},"language":"en","primary_location":{"id":"doi:10.1007/s40747-023-01185-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40747-023-01185-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40747-023-01185-2.pdf","source":{"id":"https://openalex.org/S3035462843","display_name":"Complex & Intelligent Systems","issn_l":"2198-6053","issn":["2198-6053","2199-4536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complex & Intelligent Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s40747-023-01185-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019869614","display_name":"Fuquan Li","orcid":"https://orcid.org/0000-0002-1573-597X"},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fuquan Li","raw_affiliation_strings":["School of Big Data and Computer Science, Guizhou Normal University, Guizhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science, Guizhou Normal University, Guizhou, China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112725736","display_name":"Yonghui Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghui Zhou","raw_affiliation_strings":["School of Big Data and Computer Science, Guizhou Normal University, Guizhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science, Guizhou Normal University, Guizhou, China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100602469","display_name":"Yanli Chen","orcid":"https://orcid.org/0000-0003-4452-5725"},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"YanLi Chen","raw_affiliation_strings":["School of Big Data and Computer Science, Guizhou Normal University, Guizhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4452-5725","affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science, Guizhou Normal University, Guizhou, China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089770704","display_name":"Jie Li","orcid":"https://orcid.org/0000-0002-4063-9381"},"institutions":[{"id":"https://openalex.org/I168337820","display_name":"Chongqing University of Science and Technology","ror":"https://ror.org/03n3v6d52","country_code":"CN","type":"education","lineage":["https://openalex.org/I168337820"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":["School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China","institution_ids":["https://openalex.org/I168337820"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080815140","display_name":"Zhicheng Dong","orcid":"https://orcid.org/0000-0003-2909-5720"},"institutions":[{"id":"https://openalex.org/I140786321","display_name":"Tibet University","ror":"https://ror.org/05petvd47","country_code":"CN","type":"education","lineage":["https://openalex.org/I140786321"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ZhiCheng Dong","raw_affiliation_strings":["School of Information Science and Technology, Tibet University, Lhasa, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Tibet University, Lhasa, China","institution_ids":["https://openalex.org/I140786321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069568895","display_name":"Mian Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210095439","display_name":"Guizhou Minzu University","ror":"https://ror.org/00qm4t918","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210095439"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mian Tan","raw_affiliation_strings":["Guizhou Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University, Guizhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guizhou Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University, Guizhou, China","institution_ids":["https://openalex.org/I4210095439"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5019869614"],"corresponding_institution_ids":["https://openalex.org/I154893126"],"apc_list":{"value":1320,"currency":"GBP","value_usd":1619},"apc_paid":{"value":1320,"currency":"GBP","value_usd":1619},"fwci":1.0908,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80325338,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"10","issue":"1","first_page":"705","last_page":"719"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9961000084877014,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.994700014591217,"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/computer-science","display_name":"Computer science","score":0.7325994968414307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7214841842651367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6842945218086243},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6270096302032471},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5764637589454651},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.457690566778183},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45018091797828674},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.44726812839508057},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4264725148677826},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4202066659927368},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29796943068504333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325994968414307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7214841842651367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6842945218086243},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6270096302032471},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5764637589454651},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.457690566778183},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45018091797828674},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.44726812839508057},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4264725148677826},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4202066659927368},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29796943068504333},{"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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s40747-023-01185-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40747-023-01185-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40747-023-01185-2.pdf","source":{"id":"https://openalex.org/S3035462843","display_name":"Complex & Intelligent Systems","issn_l":"2198-6053","issn":["2198-6053","2199-4536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complex & Intelligent Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bd0081063d804a63a1d3d2634b1c00a3","is_oa":true,"landing_page_url":"https://doaj.org/article/bd0081063d804a63a1d3d2634b1c00a3","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Complex & Intelligent Systems, Vol 10, Iss 1, Pp 705-719 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s40747-023-01185-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40747-023-01185-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40747-023-01185-2.pdf","source":{"id":"https://openalex.org/S3035462843","display_name":"Complex & Intelligent Systems","issn_l":"2198-6053","issn":["2198-6053","2199-4536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complex & Intelligent Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4316651334","display_name":null,"funder_award_id":"Qiankehejichu-ZK[2021]Yiban310","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4671806747","display_name":null,"funder_award_id":"[2021]","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5343761973","display_name":null,"funder_award_id":"QKHJCZK2022YB19","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7239750578","display_name":null,"funder_award_id":"2020YFC0833406","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8620774065","display_name":null,"funder_award_id":"62102112","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385478725.pdf"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1708141795","https://openalex.org/W1965606870","https://openalex.org/W1980382026","https://openalex.org/W1997596006","https://openalex.org/W2074907730","https://openalex.org/W2146353910","https://openalex.org/W2153777140","https://openalex.org/W2179019672","https://openalex.org/W2182501874","https://openalex.org/W2266694576","https://openalex.org/W2363659267","https://openalex.org/W2559870345","https://openalex.org/W2568836762","https://openalex.org/W2613594293","https://openalex.org/W2774839435","https://openalex.org/W2777658515","https://openalex.org/W2912147220","https://openalex.org/W2962850830","https://openalex.org/W2963091558","https://openalex.org/W2963420686","https://openalex.org/W2963530785","https://openalex.org/W2963787388","https://openalex.org/W2971071255","https://openalex.org/W3007891240","https://openalex.org/W3011768656","https://openalex.org/W3036032447","https://openalex.org/W3039415429","https://openalex.org/W3046194589","https://openalex.org/W3047800102","https://openalex.org/W3083240798","https://openalex.org/W3092444386","https://openalex.org/W3097988966","https://openalex.org/W3102411220","https://openalex.org/W3119042474","https://openalex.org/W3126855404","https://openalex.org/W3133700567","https://openalex.org/W3143068962","https://openalex.org/W3158080681","https://openalex.org/W3181367324","https://openalex.org/W3182674219","https://openalex.org/W3205654160","https://openalex.org/W3212386989","https://openalex.org/W4206713196","https://openalex.org/W4214666412","https://openalex.org/W4220893768","https://openalex.org/W4223981310","https://openalex.org/W4224256886","https://openalex.org/W4283732315","https://openalex.org/W4309419826","https://openalex.org/W4310895557","https://openalex.org/W4312351083","https://openalex.org/W4313892770","https://openalex.org/W4317987737","https://openalex.org/W6977229760"],"related_works":["https://openalex.org/W2788731446","https://openalex.org/W2204403038","https://openalex.org/W3152170969","https://openalex.org/W2379054866","https://openalex.org/W2370195708","https://openalex.org/W1490651872","https://openalex.org/W2139242969","https://openalex.org/W2284201331","https://openalex.org/W2095903272","https://openalex.org/W1989561795"],"abstract_inverted_index":{"Abstract":[0],"Infrared":[1],"and":[2,14,21,33,44,53,63,149,165,172,189,225],"visible":[3,45],"image":[4,46],"fusion":[5,47,94,158],"aims":[6],"to":[7,58,132,140,155,160],"generate":[8],"synthetic":[9],"images":[10],"including":[11],"salient":[12],"targets":[13],"abundant":[15],"texture":[16],"details.":[17,66],"However,":[18],"traditional":[19],"techniques":[20],"recent":[22],"deep":[23,99],"learning-based":[24],"approaches":[25],"have":[26],"faced":[27],"challenges":[28],"in":[29,92,185],"preserving":[30],"prominent":[31,84],"structures":[32],"fine-grained":[34,64],"features.":[35],"In":[36,233],"this":[37],"study,":[38],"we":[39,68,150],"propose":[40],"a":[41,70,113],"lightweight":[42],"infrared":[43],"network":[48,139],"utilizing":[49],"multi-scale":[50,226],"attention":[51,128],"modules":[52,129],"hybrid":[54,71],"dilated":[55,72],"convolutional":[56,73,117],"blocks":[57],"preserve":[59],"significant":[60],"structural":[61,227],"features":[62,86],"textural":[65],"First,":[67],"design":[69],"block":[74],"with":[75,97,236],"different":[76,135],"dilation":[77],"rates":[78],"that":[79,199,242],"enable":[80],"the":[81,89,93,121,138,146,152,157,176,204,237],"extraction":[82],"of":[83,116,123,137,145,178,220],"structure":[85],"by":[87,207,212,217,223,230],"enlarging":[88],"receptive":[90],"field":[91],"network.":[95],"Compared":[96],"other":[98,246],"learning":[100],"methods,":[101],"our":[102,179,200,243],"method":[103,181,201],"can":[104,202],"obtain":[105],"more":[106],"high-level":[107],"semantic":[108],"information":[109,144],"without":[110],"piling":[111],"up":[112],"large":[114],"number":[115],"blocks,":[118],"effectively":[119],"improving":[120],"ability":[122],"feature":[124],"representation.":[125],"Second,":[126],"distinct":[127],"are":[130],"designed":[131],"integrate":[133],"into":[134],"layers":[136],"fully":[141],"exploit":[142],"contextual":[143],"source":[147],"images,":[148],"leverage":[151],"total":[153],"loss":[154],"guide":[156],"process":[159],"focus":[161],"on":[162,195],"vital":[163],"regions":[164],"compensate":[166],"for":[167],"missing":[168],"information.":[169],"Extensive":[170],"qualitative":[171],"quantitative":[173],"experiments":[174,235],"demonstrate":[175],"superiority":[177],"proposed":[180],"over":[182],"state-of-the-art":[183],"methods":[184],"both":[186],"visual":[187],"effects":[188],"evaluation":[190],"metrics.":[191],"The":[192],"experimental":[193],"results":[194],"public":[196],"datasets":[197],"show":[198],"improve":[203],"entropy":[205],"(EN)":[206],"4.80%,":[208],"standard":[209],"deviation":[210],"(SD)":[211],"3.97%,":[213],"correlation":[214],"coefficient":[215],"(CC)":[216],"1.86%,":[218],"correlations":[219],"differences":[221],"(SCD)":[222],"9.98%,":[224],"similarity":[228],"(MS_SSIM)":[229],"5.64%,":[231],"respectively.":[232],"addition,":[234],"VIFB":[238],"dataset":[239],"further":[240],"indicate":[241],"approach":[244],"outperforms":[245],"comparable":[247],"models.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}