{"id":"https://openalex.org/W7124958001","doi":"https://doi.org/10.1109/tpami.2026.3655694","title":"A General Image Fusion Approach Exploiting Gradient Transfer Learning and Fusion Rule Unfolding","display_name":"A General Image Fusion Approach Exploiting Gradient Transfer Learning and Fusion Rule Unfolding","publication_year":2026,"publication_date":"2026-01-19","ids":{"openalex":"https://openalex.org/W7124958001","doi":"https://doi.org/10.1109/tpami.2026.3655694","pmid":"https://pubmed.ncbi.nlm.nih.gov/41553895"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2026.3655694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2026.3655694","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100413352","display_name":"Wu Wang","orcid":"https://orcid.org/0000-0002-9431-2878"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Wang","raw_affiliation_strings":["School of Computing and Artificial Intelligence, and Engineering Research Center of Intelligent Finance, Ministry of Education, Southwestern University of Finance and Economics, Chengdu, Sichuan, China"],"raw_orcid":"https://orcid.org/0000-0002-9431-2878","affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, and Engineering Research Center of Intelligent Finance, Ministry of Education, Southwestern University of Finance and Economics, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023424232","display_name":"L. Deng","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang-Jian Deng","raw_affiliation_strings":["School of Mathematical Sciences/Multi-Hazard Early Warning Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"raw_orcid":"https://orcid.org/0000-0003-3178-9772","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences/Multi-Hazard Early Warning Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101655271","display_name":"Qi Cao","orcid":"https://orcid.org/0009-0001-5477-316X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Cao","raw_affiliation_strings":["Yingcai Honors College and School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yingcai Honors College and School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043777501","display_name":"Gemine Vivone","orcid":"https://orcid.org/0000-0001-9542-0638"},"institutions":[{"id":"https://openalex.org/I4210115556","display_name":"National Research Council - Institute of Methodologies for Environmental Analysis","ror":"https://ror.org/024ye7w89","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210115556","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gemine Vivone","raw_affiliation_strings":["Institute of Methodologies for Environmental Analysis, CNR-IMAA, Tito Scalo, Italy"],"raw_orcid":"https://orcid.org/0000-0001-9542-0638","affiliations":[{"raw_affiliation_string":"Institute of Methodologies for Environmental Analysis, CNR-IMAA, Tito Scalo, Italy","institution_ids":["https://openalex.org/I4210115556"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06308537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"48","issue":"6","first_page":"6347","last_page":"6364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9860000014305115,"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":0.9860000014305115,"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":0.008200000040233135,"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":0.0006000000284984708,"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/image-fusion","display_name":"Image fusion","score":0.804099977016449},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7143999934196472},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5795000195503235},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5601000189781189},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4546999931335449},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4205999970436096},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4034000039100647},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3695000112056732}],"concepts":[{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.804099977016449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7466999888420105},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7143999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6916000247001648},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5795000195503235},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5601000189781189},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4205999970436096},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3723999857902527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C182037307","wikidata":"https://www.wikidata.org/wiki/Q17039097","display_name":"Image gradient","level":5,"score":0.3619000017642975},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C2778971668","wikidata":"https://www.wikidata.org/wiki/Q5510284","display_name":"Fusion rules","level":4,"score":0.321399986743927},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2955000102519989},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2709999978542328},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.26989999413490295},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.26420000195503235},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26409998536109924}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2026.3655694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2026.3655694","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:41553895","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41553895","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2129173210","display_name":null,"funder_award_id":"12271083","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":87,"referenced_works":["https://openalex.org/W1563797521","https://openalex.org/W1580389772","https://openalex.org/W1580436348","https://openalex.org/W1708141795","https://openalex.org/W1964641132","https://openalex.org/W1977822596","https://openalex.org/W1999034606","https://openalex.org/W2038281434","https://openalex.org/W2039465985","https://openalex.org/W2062432118","https://openalex.org/W2065428319","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2266694576","https://openalex.org/W2559870345","https://openalex.org/W2757470902","https://openalex.org/W2783573276","https://openalex.org/W2798987894","https://openalex.org/W2905307044","https://openalex.org/W2912147220","https://openalex.org/W2963134949","https://openalex.org/W2963446712","https://openalex.org/W2963787388","https://openalex.org/W2971071255","https://openalex.org/W2998012573","https://openalex.org/W2998529071","https://openalex.org/W2999838674","https://openalex.org/W3014859219","https://openalex.org/W3030380536","https://openalex.org/W3035260401","https://openalex.org/W3035467948","https://openalex.org/W3046194589","https://openalex.org/W3047464731","https://openalex.org/W3083574456","https://openalex.org/W3088623196","https://openalex.org/W3097217077","https://openalex.org/W3110670586","https://openalex.org/W3133700567","https://openalex.org/W3159235206","https://openalex.org/W3162209674","https://openalex.org/W3190808861","https://openalex.org/W3204483762","https://openalex.org/W3206506760","https://openalex.org/W3213749391","https://openalex.org/W3216987090","https://openalex.org/W4214893857","https://openalex.org/W4283360701","https://openalex.org/W4283732315","https://openalex.org/W4286361941","https://openalex.org/W4288391270","https://openalex.org/W4295916779","https://openalex.org/W4302774134","https://openalex.org/W4309730479","https://openalex.org/W4312052673","https://openalex.org/W4312349930","https://openalex.org/W4312402232","https://openalex.org/W4313009532","https://openalex.org/W4319663704","https://openalex.org/W4321766599","https://openalex.org/W4366352735","https://openalex.org/W4376131895","https://openalex.org/W4379741379","https://openalex.org/W4383899489","https://openalex.org/W4385063871","https://openalex.org/W4386076493","https://openalex.org/W4386076504","https://openalex.org/W4387546313","https://openalex.org/W4387880970","https://openalex.org/W4387969227","https://openalex.org/W4388820247","https://openalex.org/W4389491034","https://openalex.org/W4390241237","https://openalex.org/W4390873230","https://openalex.org/W4391992568","https://openalex.org/W4393240973","https://openalex.org/W4394566708","https://openalex.org/W4401200174","https://openalex.org/W4401648286","https://openalex.org/W4402215582","https://openalex.org/W4402703122","https://openalex.org/W4402753825","https://openalex.org/W4402774288","https://openalex.org/W4403183027","https://openalex.org/W4403295582","https://openalex.org/W4405953164","https://openalex.org/W4406891929","https://openalex.org/W4413158072"],"related_works":[],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,25,64,166,211],"a":[3,18,79,108,142,151,157],"deep":[4,152],"learning-based":[5],"general":[6,83],"image":[7,14,84,118,128,144,162,175,180,183,189,217,222],"fusion":[8,15,85,94,119,145,163,170,176,218],"method":[9,192,227],"is":[10],"to":[11,34],"solve":[12],"multiple":[13],"tasks":[16,103,120],"with":[17,197],"single":[19],"model,":[20,154],"thereby":[21],"facilitating":[22],"the":[23,87,114,209],"deployment":[24],"models":[26],"in":[27,156],"practical":[28],"applications.":[29],"However,":[30],"existing":[31],"methods":[32],"fail":[33],"provide":[35],"an":[36],"efficient":[37,159],"and":[38,45,93,126,147,160,185,187],"comprehensive":[39,80],"solution":[40],"from":[41],"both":[42],"model":[43,50],"training":[44],"network":[46,65,71,138,164],"design":[47],"perspectives.":[48],"Regarding":[49],"training,":[51,105],"current":[52],"approaches":[53],"cannot":[54],"effectively":[55,130],"leverage":[56,98],"complementary":[57,99,123],"information":[58,100,200],"across":[59,101],"different":[60,102,117,174],"tasks.":[61,171],"In":[62],"terms":[63],"design,":[66,139],"they":[67],"rely":[68],"on":[69,113,214],"experience-based":[70],"designs.":[72],"To":[73,97,134],"address":[74],"these":[75,132],"issues,":[76],"we":[77,106,140],"propose":[78,107],"framework":[81,111],"for":[82],"using":[86],"newly":[88],"proposed":[89],"gradient":[90],"transfer":[91],"learning":[92],"rule":[95,146],"unfolding.":[96],"during":[104],"sequential":[109],"gradient-transfer":[110],"based":[112],"idea":[115],"that":[116,127,225],"often":[121],"exhibit":[122],"structural":[124,199],"details":[125],"gradients":[129],"capture":[131],"details.":[133],"move":[135],"beyond":[136],"heuristic-based":[137],"evolved":[141],"fundamental":[143],"integrated":[148],"it":[149],"into":[150],"equilibrium":[153],"resulting":[155],"more":[158],"versatile":[161],"capable":[165],"uniformly":[167],"handling":[168],"various":[169],"Considering":[172],"three":[173],"tasks,":[177,219],"i.e.,":[178,220],"multi-focus":[179],"fusion,":[181,184,190,223],"multi-exposure":[182],"infrared":[186],"visible":[188],"our":[191,226],"not":[193],"only":[194],"produces":[195],"images":[196],"richer":[198],"but":[201],"also":[202],"achieves":[203],"highly":[204],"competitive":[205],"objective":[206],"metrics.":[207],"Furthermore,":[208],"results":[210],"generalization":[212],"experiments":[213],"previously":[215],"unseen":[216],"medical":[221],"demonstrate":[224],"significantly":[228],"outperforms":[229],"competing":[230],"approaches.":[231]},"counts_by_year":[],"updated_date":"2026-05-09T06:09:20.037420","created_date":"2026-01-21T00:00:00"}