{"id":"https://openalex.org/W4387968090","doi":"https://doi.org/10.1145/3581783.3611744","title":"NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer","display_name":"NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387968090","doi":"https://doi.org/10.1145/3581783.3611744"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3611744","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3611744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5083093046","display_name":"Yun Liu","orcid":"https://orcid.org/0000-0002-9567-5531"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yun Liu","raw_affiliation_strings":["Southwest University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101582860","display_name":"Zhongsheng Yan","orcid":"https://orcid.org/0000-0003-0492-3841"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongsheng Yan","raw_affiliation_strings":["Southwest University, Chongqing , China"],"affiliations":[{"raw_affiliation_string":"Southwest University, Chongqing , China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000301628","display_name":"Sixiang Chen","orcid":"https://orcid.org/0009-0003-6837-886X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sixiang Chen","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387912","display_name":"Ye Tian","orcid":"https://orcid.org/0000-0002-8255-2997"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian Ye","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057999649","display_name":"Wenqi Ren","orcid":"https://orcid.org/0000-0001-5481-653X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Ren","raw_affiliation_strings":["Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081351856","display_name":"Erkang Chen","orcid":"https://orcid.org/0000-0003-1577-1732"},"institutions":[{"id":"https://openalex.org/I161346416","display_name":"Jimei University","ror":"https://ror.org/03hknyb50","country_code":"CN","type":"education","lineage":["https://openalex.org/I161346416"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Erkang Chen","raw_affiliation_strings":["Jimei University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Jimei University, Xiamen, China","institution_ids":["https://openalex.org/I161346416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5083093046"],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":9.2678,"has_fulltext":false,"cited_by_count":77,"citation_normalized_percentile":{"value":0.98675332,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4119","last_page":"4128"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9958999752998352,"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":0.9955000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8152918219566345},{"id":"https://openalex.org/keywords/haze","display_name":"Haze","score":0.6594259142875671},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6565094590187073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5991798639297485},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4243672788143158},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4219900071620941},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3494924306869507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8152918219566345},{"id":"https://openalex.org/C79974267","wikidata":"https://www.wikidata.org/wiki/Q643546","display_name":"Haze","level":2,"score":0.6594259142875671},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6565094590187073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5991798639297485},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4243672788143158},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4219900071620941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3494924306869507},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"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/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"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":1,"locations":[{"id":"doi:10.1145/3581783.3611744","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3611744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.75,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G7979153096","display_name":null,"funder_award_id":"2021J01867","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1979510767","https://openalex.org/W2028763589","https://openalex.org/W2063971703","https://openalex.org/W2078807908","https://openalex.org/W2102166818","https://openalex.org/W2128254161","https://openalex.org/W2133665775","https://openalex.org/W2147318913","https://openalex.org/W2156936307","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2256362396","https://openalex.org/W2519481857","https://openalex.org/W2746139371","https://openalex.org/W2767420414","https://openalex.org/W2779176852","https://openalex.org/W2892628644","https://openalex.org/W2901543290","https://openalex.org/W2981980942","https://openalex.org/W2996367318","https://openalex.org/W2998249728","https://openalex.org/W2999905431","https://openalex.org/W3008316673","https://openalex.org/W3035731588","https://openalex.org/W3091987649","https://openalex.org/W3093348923","https://openalex.org/W3096609285","https://openalex.org/W3118649694","https://openalex.org/W3129534753","https://openalex.org/W3173269149","https://openalex.org/W3174620792","https://openalex.org/W3176096490","https://openalex.org/W3180034634","https://openalex.org/W3208626099","https://openalex.org/W4206628548","https://openalex.org/W4214745154","https://openalex.org/W4221166782","https://openalex.org/W4283119285","https://openalex.org/W4289537807","https://openalex.org/W4292258243","https://openalex.org/W4292794159","https://openalex.org/W4296904787","https://openalex.org/W4301409532","https://openalex.org/W4304013729","https://openalex.org/W4304080734","https://openalex.org/W4304084089","https://openalex.org/W4304084103","https://openalex.org/W4304091766","https://openalex.org/W4312556940","https://openalex.org/W4312644870","https://openalex.org/W4312746248","https://openalex.org/W4312862560","https://openalex.org/W4312918841","https://openalex.org/W4312938066","https://openalex.org/W4313026854","https://openalex.org/W4313071068","https://openalex.org/W4372270072","https://openalex.org/W4375869442","https://openalex.org/W4386066405","https://openalex.org/W4386075792","https://openalex.org/W4386076213","https://openalex.org/W4386083159","https://openalex.org/W6851402089"],"related_works":["https://openalex.org/W2397673276","https://openalex.org/W2318437963","https://openalex.org/W2348696601","https://openalex.org/W2394444438","https://openalex.org/W2377355001","https://openalex.org/W2387386748","https://openalex.org/W2377493372","https://openalex.org/W2388134306","https://openalex.org/W2371905190","https://openalex.org/W2374327637"],"abstract_inverted_index":{"Nighttime":[0],"image":[1,36],"dehazing":[2,37],"is":[3],"a":[4,153],"challenging":[5],"task":[6],"due":[7],"to":[8,49,93,103,132,145,159],"the":[9,81,90,95,101,108,112,129,134,143,161,176],"presence":[10],"of":[11,14,72,178,189],"multiple":[12],"types":[13],"adverse":[15],"degrading":[16],"effects":[17],"including":[18],"glow,":[19],"haze,":[20],"blur,":[21],"noise,":[22],"color":[23],"distortion,":[24],"and":[25,77,126,172,192],"so":[26],"on.":[27],"However,":[28],"most":[29],"previous":[30],"studies":[31],"mainly":[32],"focus":[33],"on":[34,169],"daytime":[35],"or":[38],"partial":[39],"degradations":[40],"presented":[41],"in":[42,187],"nighttime":[43,63,120,163,183],"hazy":[44,121],"scenes,":[45],"which":[46,99],"may":[47],"lead":[48],"unsatisfactory":[50],"restoration":[51],"results.":[52],"In":[53,148],"this":[54],"paper,":[55],"we":[56,84,110,150],"propose":[57,152],"an":[58],"end-to-end":[59],"transformer-based":[60],"framework":[61],"for":[62],"haze":[64,164,184],"removal,":[65],"called":[66,157],"NightHazeFormer.":[67],"Our":[68],"proposed":[69],"approach":[70],"consists":[71],"two":[73,86],"stages:":[74],"supervised":[75],"pre-training":[76,82],"semi-supervised":[78,138],"fine-tuning.":[79],"During":[80],"stage,":[83],"introduce":[85],"powerful":[87],"priors":[88],"into":[89,128],"transformer":[91],"decoder":[92],"generate":[94],"non-learnable":[96],"prior":[97],"queries,":[98],"guide":[100],"model":[102],"extract":[104],"specific":[105],"degradations.":[106],"For":[107],"fine-tuning,":[109],"combine":[111],"generated":[113],"pseudo":[114],"ground":[115],"truths":[116],"with":[117],"input":[118],"real-world":[119,162,173],"images":[122,125],"as":[123],"paired":[124],"feed":[127],"synthetic":[130,155,171],"domain":[131],"fine-tune":[133],"pre-trained":[135],"model.":[136],"This":[137],"fine-tuning":[139],"paradigm":[140],"helps":[141],"improve":[142],"generalization":[144],"real":[146],"domain.":[147],"addition,":[149],"also":[151],"large-scale":[154],"dataset":[156],"UNREAL-NH,":[158],"simulate":[160],"scenarios":[165],"comprehensively.":[166],"Extensive":[167],"experiments":[168],"several":[170],"datasets":[174],"demonstrate":[175],"superiority":[177],"our":[179],"NightHazeFormer":[180],"over":[181],"state-of-the-art":[182],"removal":[185],"methods":[186],"terms":[188],"both":[190],"visually":[191],"quantitatively.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":38},{"year":2023,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}