{"id":"https://openalex.org/W2791451704","doi":"https://doi.org/10.1109/vcip.2017.8305035","title":"Deep fully convolutional regression networks for single image haze removal","display_name":"Deep fully convolutional regression networks for single image haze removal","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2791451704","doi":"https://doi.org/10.1109/vcip.2017.8305035","mag":"2791451704"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2017.8305035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","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/A5103191607","display_name":"Xi Zhao","orcid":"https://orcid.org/0000-0001-9105-5872"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Zhao","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013877857","display_name":"Keyan Wang","orcid":"https://orcid.org/0000-0002-9545-718X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keyan Wang","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067798266","display_name":"Yunsong Li","orcid":"https://orcid.org/0000-0002-0234-6270"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunsong Li","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100734649","display_name":"Jiaojiao Li","orcid":"https://orcid.org/0000-0002-0470-9469"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaojiao Li","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.8313,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.83212815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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":0.9979000091552734,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9976000189781189,"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":0.8368275761604309},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8145701885223389},{"id":"https://openalex.org/keywords/haze","display_name":"Haze","score":0.7591488361358643},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7440198659896851},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7047098875045776},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5879915952682495},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5565162897109985},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.5501287579536438},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4725715219974518},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46345752477645874},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45680516958236694},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4342808723449707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07052817940711975},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06721469759941101}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8368275761604309},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8145701885223389},{"id":"https://openalex.org/C79974267","wikidata":"https://www.wikidata.org/wiki/Q643546","display_name":"Haze","level":2,"score":0.7591488361358643},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7440198659896851},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7047098875045776},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5879915952682495},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5565162897109985},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.5501287579536438},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4725715219974518},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46345752477645874},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45680516958236694},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4342808723449707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07052817940711975},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06721469759941101},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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":1,"locations":[{"id":"doi:10.1109/vcip.2017.8305035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324173","display_name":"Natural Science Foundation of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1836465849","https://openalex.org/W2125416623","https://openalex.org/W2128254161","https://openalex.org/W2132947399","https://openalex.org/W2147318913","https://openalex.org/W2155893237","https://openalex.org/W2256362396","https://openalex.org/W2519481857","https://openalex.org/W2582488595","https://openalex.org/W2949117887","https://openalex.org/W2963591054","https://openalex.org/W2991006905","https://openalex.org/W6605121731"],"related_works":["https://openalex.org/W2318437963","https://openalex.org/W2348696601","https://openalex.org/W2394444438","https://openalex.org/W2377355001","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Haze":[0],"removal":[1,139],"for":[2,87,118],"a":[3,10],"single":[4],"image":[5,22,106,119],"is":[6,25,94],"known":[7],"to":[8,71,78],"be":[9],"challenging":[11],"ill-posed":[12],"problem":[13],"in":[14],"computer":[15],"vision.":[16],"The":[17,33,152],"performance":[18,164],"of":[19,30,102],"existing":[20,136],"prior-based":[21],"dehazing":[23,120,163],"methods":[24],"limited":[26],"by":[27],"the":[28,45,51,61,73,135,166],"effectiveness":[29],"hand-designed":[31],"features.":[32],"emerging":[34],"con-volutional":[35],"neural":[36],"network":[37,86,93,117],"(CNN)":[38],"based":[39],"approaches":[40,140],"can":[41],"remove":[42],"haze":[43,138],"with":[44,141],"automatically":[46],"learned":[47],"intrinsic":[48],"mapping":[49],"between":[50],"input":[52,101],"hazy":[53,105],"images":[54,64,146],"and":[55,107,114,147],"their":[56],"corresponding":[57],"transmission":[58,90,110],"maps,":[59],"but":[60],"recovered":[62],"haze-free":[63],"sometimes":[65],"are":[66],"still":[67],"unsatisfactory.":[68],"In":[69,130],"order":[70],"improve":[72],"dehazed":[74],"images,":[75],"we":[76,122,132],"aim":[77],"develop":[79,123],"an":[80,95],"effective":[81],"deep":[82,116],"fully":[83,133],"convolutional":[84],"regression":[85,97,159],"more":[88],"accurate":[89],"estimation.":[91],"Our":[92],"end-to-end":[96],"system":[98],"which":[99],"take":[100],"arbitrary":[103],"size":[104],"predict":[108],"correspondingly-sized":[109],"map.":[111],"To":[112],"train":[113],"evaluate":[115],"efficiently,":[121],"new":[124],"outdoor":[125],"synthetic":[126],"training":[127],"set":[128],"respectively.":[129],"addition,":[131],"compare":[134],"CNN-based":[137],"our":[142,148,157],"algorithm":[143],"on":[144],"real-world":[145],"synthesized":[149],"benchmark":[150],"dataset.":[151],"experimental":[153],"results":[154],"demonstrate":[155],"that":[156],"trained":[158],"model":[160],"achieves":[161],"superior":[162],"than":[165],"current":[167],"state-of-the-art":[168],"methods.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
