{"id":"https://openalex.org/W7125928407","doi":"https://doi.org/10.1109/jiot.2026.3658838","title":"Wavelet-Attention Transformer-Based Traffic Image Dehazing for Intelligent and Connected Transportation Systems","display_name":"Wavelet-Attention Transformer-Based Traffic Image Dehazing for Intelligent and Connected Transportation Systems","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125928407","doi":"https://doi.org/10.1109/jiot.2026.3658838"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2026.3658838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3658838","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5012022182","display_name":"Chenxin Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenxin Wei","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1089-8075","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124057922","display_name":"Zhibin Li","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhibin Li","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-7192-6853","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124124047","display_name":"Shunchao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunchao Wang","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7826-5880","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124117972","display_name":"Meng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Meng Li","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University, Jurong West, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Jurong West, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100372433","display_name":"Bin Wang","orcid":"https://orcid.org/0009-0001-7917-9327"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingtong Wang","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124070852","display_name":"Huihuang Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huihuang Zhu","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0007-0208-2539","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5012022182"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16098323,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"8","first_page":"16102","last_page":"16116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.8883000016212463,"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":0.8883000016212463,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.035999998450279236,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.01080000028014183,"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/robustness","display_name":"Robustness (evolution)","score":0.7182999849319458},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.633400022983551},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5001999735832214},{"id":"https://openalex.org/keywords/haze","display_name":"Haze","score":0.49959999322891235},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4578999876976013},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.38029998540878296},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.36340001225471497},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.36309999227523804},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3625999987125397}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8622999787330627},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7182999849319458},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6414999961853027},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.633400022983551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029999852180481},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5001999735832214},{"id":"https://openalex.org/C79974267","wikidata":"https://www.wikidata.org/wiki/Q643546","display_name":"Haze","level":2,"score":0.49959999322891235},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4578999876976013},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38499999046325684},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C76935873","wikidata":"https://www.wikidata.org/wiki/Q209121","display_name":"Image sensor","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C89720835","wikidata":"https://www.wikidata.org/wiki/Q1531701","display_name":"Global illumination","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.25619998574256897}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jiot.2026.3658838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3658838","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/211943","is_oa":false,"landing_page_url":"https://hdl.handle.net/10356/211943","pdf_url":null,"source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5499202013015747,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2792856952","display_name":null,"funder_award_id":"SJCX25_0130","funder_id":"https://openalex.org/F4320334982","funder_display_name":"Basic Research Program of Jiangsu Province"},{"id":"https://openalex.org/G6891822477","display_name":null,"funder_award_id":"52272331","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8939083471","display_name":null,"funder_award_id":"52402401","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/F4320334982","display_name":"Basic Research Program of Jiangsu Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"intelligent":[1],"and":[2,18,26,34,60,104,110,127,134,166,171,174],"connected":[3],"transportation":[4],"systems,":[5],"surveillance":[6],"cameras":[7],"serve":[8],"as":[9],"critical":[10],"perception":[11],"devices":[12],"for":[13],"real-time":[14],"monitoring":[15],"of":[16,71,153,161],"traffic":[17,138,155],"lane":[19],"conditions.":[20],"However,":[21],"haze":[22,147],"causes":[23],"light":[24],"scattering":[25],"contrast":[27],"reduction,":[28],"degrading":[29],"high-frequency":[30,58,88,105],"details":[31,59,106],"(edges,":[32],"textures)":[33],"semantic":[35,102],"information":[36],"(traffic":[37],"participants)":[38],"in":[39,48,63,107,137,168],"camera":[40],"images.":[41],"Existing":[42],"methods":[43,67],"face":[44],"two":[45],"major":[46],"challenges":[47],"handling":[49],"hazy":[50],"images:":[51],"(1)":[52],"traditional":[53],"CNNs":[54],"struggle":[55],"to":[56,86],"recover":[57],"structures":[61],"lost":[62],"haze;":[64],"(2)":[65],"existing":[66],"lack":[68],"explicit":[69],"modeling":[70],"spatially":[72],"non-uniform":[73,146],"haze.":[74],"To":[75],"resolve":[76],"these":[77],"problems,":[78],"this":[79,91],"paper":[80,92],"uses":[81],"cascaded":[82],"wavelet":[83],"transform":[84],"convolution":[85],"reconstruct":[87],"details.":[89],"Subsequently,":[90],"designs":[93],"a":[94],"dual":[95],"residual":[96,128],"attention":[97,126],"mechanism":[98],"that":[99,143],"emphasizes":[100],"crucial":[101],"regions":[103],"both":[108],"channel":[109],"spatial":[111],"dimensions.":[112],"Lastly,":[113],"global":[114],"dehazing":[115],"is":[116],"achieved":[117],"by":[118,177],"Swin":[119],"Transformer\u2013based":[120],"image":[121],"modeling,":[122],"where":[123],"hierarchical":[124],"window":[125],"connections":[129],"effectively":[130],"capture":[131],"multi-scale":[132],"features":[133],"long-range":[135],"dependencies":[136],"scenes.":[139],"Experimental":[140],"results":[141],"show":[142],"the":[144,151],"proposed":[145],"removal":[148],"model":[149],"improves":[150],"robustness":[152],"IoT-enabled":[154],"monitoring.":[156],"It":[157],"achieves":[158],"average":[159],"improvements":[160],"at":[162],"least":[163],"10.45%,":[164],"4.80%,":[165],"10.77%":[167],"PSNR,":[169],"SSIM,":[170],"VSNR,":[172],"respectively,":[173],"reduces":[175],"LPIPS":[176],"no":[178],"less":[179],"than":[180],"20.44%.":[181]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-01-29T00:00:00"}
