{"id":"https://openalex.org/W2970073937","doi":"https://doi.org/10.3390/s19173727","title":"Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility","display_name":"Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility","publication_year":2019,"publication_date":"2019-08-28","ids":{"openalex":"https://openalex.org/W2970073937","doi":"https://doi.org/10.3390/s19173727","mag":"2970073937","pmid":"https://pubmed.ncbi.nlm.nih.gov/31466378"},"language":"en","primary_location":{"id":"doi:10.3390/s19173727","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s19173727","pdf_url":"https://www.mdpi.com/1424-8220/19/17/3727/pdf?version=1567079953","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/19/17/3727/pdf?version=1567079953","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000959943","display_name":"Ivana Shopovska","orcid":"https://orcid.org/0000-0002-8487-9598"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Ivana Shopovska","raw_affiliation_strings":["TELIN-IPI, Ghent University - imec, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium"],"raw_orcid":"https://orcid.org/0000-0002-8487-9598","affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University - imec, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028474463","display_name":"Ljubomir Jovanov","orcid":"https://orcid.org/0000-0001-8790-1116"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Ljubomir Jovanov","raw_affiliation_strings":["TELIN-IPI, Ghent University - imec, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium"],"raw_orcid":"https://orcid.org/0000-0001-8790-1116","affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University - imec, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071483672","display_name":"Wilfried Philips","orcid":"https://orcid.org/0000-0003-4456-4353"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Wilfried Philips","raw_affiliation_strings":["TELIN-IPI, Ghent University - imec, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELIN-IPI, Ghent University - imec, St-Pietersnieuwstraat 41, B-9000 Gent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5000959943"],"corresponding_institution_ids":["https://openalex.org/I32597200"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":3.9007,"has_fulltext":true,"cited_by_count":68,"citation_normalized_percentile":{"value":0.93919707,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"19","issue":"17","first_page":"3727","last_page":"3727"},"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":0.9990000128746033,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7764606475830078},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7214659452438354},{"id":"https://openalex.org/keywords/visibility","display_name":"Visibility","score":0.7210884690284729},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.68006432056427},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.64628666639328},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6229150891304016},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5975452661514282},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.569231390953064},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5525621175765991},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.510585367679596},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.49222293496131897},{"id":"https://openalex.org/keywords/night-vision","display_name":"Night vision","score":0.44413551688194275},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4407814145088196},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43047231435775757},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14856600761413574},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.07860541343688965}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7764606475830078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214659452438354},{"id":"https://openalex.org/C123403432","wikidata":"https://www.wikidata.org/wiki/Q654068","display_name":"Visibility","level":2,"score":0.7210884690284729},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.68006432056427},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.64628666639328},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6229150891304016},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5975452661514282},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.569231390953064},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5525621175765991},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.510585367679596},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.49222293496131897},{"id":"https://openalex.org/C2983470273","wikidata":"https://www.wikidata.org/wiki/Q5353651","display_name":"Night vision","level":2,"score":0.44413551688194275},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4407814145088196},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43047231435775757},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14856600761413574},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.07860541343688965},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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":[{"descriptor_ui":"D000069636","descriptor_name":"Pedestrians","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069636","descriptor_name":"Pedestrians","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069636","descriptor_name":"Pedestrians","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001334","descriptor_name":"Automobile Driving","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008029","descriptor_name":"Lighting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008029","descriptor_name":"Lighting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008029","descriptor_name":"Lighting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014785","descriptor_name":"Vision, Ocular","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014785","descriptor_name":"Vision, Ocular","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014785","descriptor_name":"Vision, Ocular","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.3390/s19173727","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s19173727","pdf_url":"https://www.mdpi.com/1424-8220/19/17/3727/pdf?version=1567079953","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:31466378","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31466378","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":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:archive.ugent.be:8626238","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-8626238","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1424-8220","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:b87a357505eb481789a312cd09035a48","is_oa":true,"landing_page_url":"https://doaj.org/article/b87a357505eb481789a312cd09035a48","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 19, Iss 17, p 3727 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/19/17/3727/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/s19173727","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:6749306","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6749306","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s19173727","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s19173727","pdf_url":"https://www.mdpi.com/1424-8220/19/17/3727/pdf?version=1567079953","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5699999928474426,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970073937.pdf","grobid_xml":"https://content.openalex.org/works/W2970073937.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W826189461","https://openalex.org/W1536680647","https://openalex.org/W1556798100","https://openalex.org/W1628236353","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1910108985","https://openalex.org/W1963882359","https://openalex.org/W1964641132","https://openalex.org/W1971342888","https://openalex.org/W1980382026","https://openalex.org/W2037328649","https://openalex.org/W2089612635","https://openalex.org/W2091484864","https://openalex.org/W2099471712","https://openalex.org/W2102605133","https://openalex.org/W2116702374","https://openalex.org/W2154751766","https://openalex.org/W2179019672","https://openalex.org/W2194775991","https://openalex.org/W2256359941","https://openalex.org/W2266694576","https://openalex.org/W2270689279","https://openalex.org/W2322691633","https://openalex.org/W2415234561","https://openalex.org/W2555361934","https://openalex.org/W2559295071","https://openalex.org/W2559728122","https://openalex.org/W2570343428","https://openalex.org/W2576508765","https://openalex.org/W2589745805","https://openalex.org/W2608096492","https://openalex.org/W2608295741","https://openalex.org/W2610070095","https://openalex.org/W2613718673","https://openalex.org/W2735436330","https://openalex.org/W2741620214","https://openalex.org/W2744070429","https://openalex.org/W2772136803","https://openalex.org/W2791697444","https://openalex.org/W2791779647","https://openalex.org/W2798018774","https://openalex.org/W2809216229","https://openalex.org/W2809795042","https://openalex.org/W2811183975","https://openalex.org/W2902314041","https://openalex.org/W2911345285","https://openalex.org/W2912147220","https://openalex.org/W2949847849","https://openalex.org/W2952424733","https://openalex.org/W2953106684","https://openalex.org/W2963188557","https://openalex.org/W2964065910","https://openalex.org/W3102515681"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2213018794","https://openalex.org/W2096892199","https://openalex.org/W3016063509"],"abstract_inverted_index":{"Reliable":[0],"vision":[1],"in":[2,37,48,88,113,197,207,222],"challenging":[3,114],"illumination":[4],"conditions":[5,184],"is":[6,93,123,218,249],"one":[7],"of":[8,12,32,44,120,131,163,195,216],"the":[9,18,42,45,124,140,144,164,171,190,193,198,214,244],"crucial":[10],"requirements":[11],"future":[13],"autonomous":[14],"automotive":[15],"systems.":[16],"In":[17,52],"last":[19],"decade,":[20],"thermal":[21,46,63],"cameras":[22,47],"have":[23],"become":[24],"more":[25,102,203],"easily":[26],"accessible":[27],"to":[28,76,94,109,126,147,158,229],"a":[29,57,105,110,136,175],"larger":[30],"number":[31],"researchers.":[33],"This":[34],"has":[35],"resulted":[36],"numerous":[38],"studies":[39],"which":[40],"confirmed":[41],"benefits":[43],"limited":[49],"visibility":[50,115,215],"conditions.":[51,116],"this":[53,121],"paper,":[54],"we":[55,232],"propose":[56],"learning-based":[58],"method":[59],"for":[60,134],"visible":[61],"and":[62,143,152,167,186,201,225,237],"image":[64,150,180,258],"fusion":[65,239],"that":[66,99,189,213,241,248,254],"focuses":[67],"on":[68,128,243,256],"generating":[69],"fused":[70,145],"images":[71,98],"with":[72,252],"high":[73],"visual":[74],"similarity":[75,137],"regular":[77,106],"truecolor":[78],"(red-green-blue":[79],"or":[80],"RGB)":[81],"images,":[82],"while":[83,247],"introducing":[84],"new":[85],"informative":[86,103],"details":[87],"pedestrian":[89,155,245],"regions.":[90],"The":[91,117],"goal":[92],"create":[95],"natural,":[96],"intuitive":[97],"would":[100],"be":[101],"than":[104],"RGB":[107,141],"camera":[108],"human":[111,165],"driver":[112],"main":[118],"novelty":[119],"paper":[122],"idea":[125],"rely":[127],"two":[129],"types":[130],"objective":[132],"functions":[133],"optimization:":[135],"metric":[138],"between":[139],"input":[142],"output":[146],"achieve":[148],"natural":[149],"appearance;":[151],"an":[153],"auxiliary":[154],"detection":[156],"error":[157],"help":[159],"defining":[160],"relevant":[161],"features":[162],"appearance":[166,194],"blending":[168],"them":[169],"into":[170],"output.":[172],"We":[173],"train":[174],"convolutional":[176],"neural":[177],"network":[178,191],"using":[179],"samples":[181],"from":[182],"variable":[183],"(day":[185],"night)":[187],"so":[188],"learns":[192],"humans":[196],"different":[199],"modalities":[200],"creates":[202],"robust":[204],"results":[205],"applicable":[206],"realistic":[208],"situations.":[209],"Our":[210],"experiments":[211],"show":[212],"pedestrians":[217],"noticeably":[219],"improved":[220],"especially":[221],"dark":[223],"regions":[224],"at":[226],"night.":[227],"Compared":[228],"existing":[230],"methods":[231,253],"can":[233],"better":[234],"learn":[235],"context":[236],"define":[238],"rules":[240],"focus":[242,255],"appearance,":[246],"not":[250],"guaranteed":[251],"low-level":[257],"quality":[259],"metrics.":[260]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
