{"id":"https://openalex.org/W2921085631","doi":"https://doi.org/10.23919/apsipa.2018.8659733","title":"Single Image Dehazing via Deep Learning-based Image Restoration","display_name":"Single Image Dehazing via Deep Learning-based Image Restoration","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2921085631","doi":"https://doi.org/10.23919/apsipa.2018.8659733","mag":"2921085631"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659733","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5111711003","display_name":"Chia-Hung Yeh","orcid":null},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Chia-Hung Yeh","raw_affiliation_strings":["Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061791819","display_name":"Chih-Hsiang Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Hsiang Huang","raw_affiliation_strings":["Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106664802","display_name":"Li\u2010Wei Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I75357094","display_name":"National Yunlin University of Science and Technology","ror":"https://ror.org/04qkq2m54","country_code":"TW","type":"education","lineage":["https://openalex.org/I75357094"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Li-Wei Kang","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan","institution_ids":["https://openalex.org/I75357094"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102792940","display_name":"Min-Hui Lin","orcid":"https://orcid.org/0000-0001-8357-5413"},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Min-Hui Lin","raw_affiliation_strings":["Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111711003"],"corresponding_institution_ids":["https://openalex.org/I142974352"],"apc_list":null,"apc_paid":null,"fwci":1.2535,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.85398327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1609","last_page":"1615"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9975000023841858,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.996399998664856,"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/haze","display_name":"Haze","score":0.8537805676460266},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7605373859405518},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7142038345336914},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6842479705810547},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.6740360260009766},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6121978163719177},{"id":"https://openalex.org/keywords/snow-removal","display_name":"Snow removal","score":0.5388990640640259},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5205217599868774},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5013201236724854},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4407515525817871},{"id":"https://openalex.org/keywords/snow","display_name":"Snow","score":0.3327430486679077},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.31560826301574707},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09942731261253357},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06790456175804138},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06045973300933838}],"concepts":[{"id":"https://openalex.org/C79974267","wikidata":"https://www.wikidata.org/wiki/Q643546","display_name":"Haze","level":2,"score":0.8537805676460266},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7605373859405518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7142038345336914},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6842479705810547},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.6740360260009766},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6121978163719177},{"id":"https://openalex.org/C2781290007","wikidata":"https://www.wikidata.org/wiki/Q3044530","display_name":"Snow removal","level":3,"score":0.5388990640640259},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5205217599868774},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5013201236724854},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4407515525817871},{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.3327430486679077},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.31560826301574707},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09942731261253357},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06790456175804138},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06045973300933838},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/apsipa.2018.8659733","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1508652512","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1965883646","https://openalex.org/W1970821875","https://openalex.org/W1992687477","https://openalex.org/W2002299629","https://openalex.org/W2003709967","https://openalex.org/W2017305479","https://openalex.org/W2028990532","https://openalex.org/W2065002911","https://openalex.org/W2066637406","https://openalex.org/W2114867966","https://openalex.org/W2121396509","https://openalex.org/W2121880036","https://openalex.org/W2125188192","https://openalex.org/W2128254161","https://openalex.org/W2133665775","https://openalex.org/W2146837664","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2256362396","https://openalex.org/W2494763982","https://openalex.org/W2519481857","https://openalex.org/W2774250990","https://openalex.org/W2779176852","https://openalex.org/W2919115771","https://openalex.org/W2963780738","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6684191040","https://openalex.org/W6726979445"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2999805992","https://openalex.org/W4291897433","https://openalex.org/W3011074480","https://openalex.org/W2130228941"],"abstract_inverted_index":{"Images/videos":[0],"captured":[1],"from":[2],"outdoor":[3,27],"visual":[4],"devices":[5],"are":[6],"usually":[7],"degraded":[8],"by":[9,66,102,135],"turbid":[10],"media,":[11],"such":[12],"as":[13],"haze,":[14],"smoke,":[15],"fog,":[16],"rain,":[17],"and":[18,60,93,114,140],"snow.":[19],"Haze":[20],"is":[21,133],"the":[22,31,74,77,80,87,91,94,119,129,137,141,153],"most":[23,67],"common":[24],"one":[25,64],"in":[26],"scenes":[28],"due":[29],"to":[30,72],"atmosphere":[32],"conditions.":[33],"This":[34],"paper":[35],"presents":[36],"a":[37,104,127],"deep":[38],"learning-based":[39],"architecture":[40],"for":[41,110],"single":[42],"image":[43,46,59,81,89,132,144],"dehazing":[44],"via":[45],"restoration.":[47],"instead":[48],"of":[49,57,79,152],"learning":[50,103],"an":[51],"end-to-end":[52],"mapping":[53,111],"between":[54,112],"each":[55],"pair":[56],"hazy":[58,88,113],"its":[61],"corresponding":[62],"haze-free":[63,115],"adopted":[65],"existing":[68],"approaches,":[69],"we":[70],"propose":[71],"transform":[73],"problem":[75],"into":[76,90],"restoration":[78],"base":[82,92,116,139],"component.":[83],"By":[84],"first":[85],"decomposing":[86],"detail":[95,120,143],"components,":[96,117],"haze":[97],"removal":[98],"can":[99,122],"be":[100,123],"achieved":[101],"CNN":[105],"(convolutional":[106],"neural":[107],"network)":[108],"only":[109],"while":[118],"component":[121],"further":[124],"enhanced.":[125],"As":[126],"result,":[128],"final":[130],"dehazed":[131],"obtained":[134],"integrating":[136],"haze-removed":[138],"enhanced":[142],"components.":[145],"Experimental":[146],"results":[147],"have":[148],"demonstrated":[149],"good":[150],"efficacy":[151],"proposed":[154],"method,":[155],"compared":[156],"with":[157],"state-of-the-art":[158],"results.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}