{"id":"https://openalex.org/W4385275484","doi":"https://doi.org/10.1145/3594409.3594410","title":"Image Defogging Method based on Multi-Scale and Frequency Domain Features","display_name":"Image Defogging Method based on Multi-Scale and Frequency Domain Features","publication_year":2023,"publication_date":"2023-03-03","ids":{"openalex":"https://openalex.org/W4385275484","doi":"https://doi.org/10.1145/3594409.3594410"},"language":"en","primary_location":{"id":"doi:10.1145/3594409.3594410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594409.3594410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Innovation in Artificial Intelligence","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/A5031137705","display_name":"Haobo Wang","orcid":"https://orcid.org/0009-0007-4606-9491"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haobo Wang","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102931693","display_name":"Yinghua Zhou","orcid":"https://orcid.org/0000-0001-8025-047X"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghua Zhou","raw_affiliation_strings":["Chongqing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Chongqing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031137705"],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07615859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"66","last_page":"71"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9980999827384949,"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":0.9937000274658203,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7336428761482239},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6923940181732178},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.620021641254425},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6136822700500488},{"id":"https://openalex.org/keywords/brightness","display_name":"Brightness","score":0.5942953824996948},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.5459432601928711},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5292510390281677},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5201883316040039},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4794993996620178},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.4595663845539093},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45001477003097534},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41477614641189575},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4136538505554199},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.1394074559211731},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09901633858680725}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7336428761482239},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6923940181732178},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.620021641254425},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6136822700500488},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.5942953824996948},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5459432601928711},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5292510390281677},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5201883316040039},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4794993996620178},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4595663845539093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45001477003097534},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41477614641189575},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4136538505554199},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.1394074559211731},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09901633858680725},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594409.3594410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594409.3594410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Innovation in Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1676314349","https://openalex.org/W1996021349","https://openalex.org/W2056862729","https://openalex.org/W2114551416","https://openalex.org/W2128254161","https://openalex.org/W2156936307","https://openalex.org/W2256362396","https://openalex.org/W2795143997","https://openalex.org/W2809287713","https://openalex.org/W2962754725","https://openalex.org/W2963928582","https://openalex.org/W3066471605","https://openalex.org/W3099004229","https://openalex.org/W6602670149"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W4243305035","https://openalex.org/W1537496349","https://openalex.org/W2379407973","https://openalex.org/W2350267540"],"abstract_inverted_index":{"Due":[0],"to":[1,68,86,115,145,167,175],"the":[2,8,16,28,39,69,78,126,133,137,141,147,150,163],"existence":[3],"of":[4,18,43,54,73,80,120,125,140,149,172],"atmospheric":[5,44],"particles":[6,48],"in":[7,21,170],"air":[9],"(such":[10],"as":[11],"dust,":[12],"colloids,":[13],"raindrops,":[14],"etc.),":[15],"quality":[17],"images":[19],"taken":[20],"outdoor":[22],"scenes":[23,56],"is":[24,165],"usually":[25,57],"poor,":[26],"and":[27,41,61,71,76,110,117,180],"visual":[29],"effect":[30],"will":[31],"decline":[32],"a":[33,99],"lot.":[34],"The":[35,123,159],"phenomena":[36],"caused":[37],"by":[38,46,131],"absorption":[40],"refraction":[42],"light":[45],"these":[47,55],"include":[49],"fog,":[50],"smoke,":[51],"etc.":[52],"Images":[53],"show":[58,161],"brightness":[59],"increase":[60,77],"contrast":[62],"decrease,":[63],"which":[64,103],"are":[65,128,154],"not":[66],"conducive":[67],"identification":[70],"extraction":[72,114],"image":[74,82,94,127],"features":[75,119],"difficulty":[79],"subsequent":[81],"processing.":[83],"In":[84,143],"order":[85,144],"solve":[87],"this":[88,90],"problem,":[89],"paper":[91],"proposes":[92],"an":[93],"defogging":[95],"method":[96],"based":[97],"on":[98,156],"deep":[100],"neural":[101],"network,":[102],"employs":[104],"convolutions,":[105],"multi-scale":[106],"feature":[107,113],"attention":[108],"mechanism":[109],"frequency":[111],"domain":[112],"extract":[116],"fuse":[118],"different":[121],"scales.":[122],"details":[124,134],"further":[129],"restored":[130],"using":[132],"extracted":[135],"from":[136],"high-frequency":[138],"components":[139],"image.":[142],"verify":[146],"performance":[148],"proposed":[151],"method,":[152],"experiments":[153,160],"conducted":[155],"RESIDE":[157],"datasets.":[158],"that":[162],"model":[164],"superior":[166],"other":[168],"models":[169],"terms":[171],"peak":[173],"signal":[174],"noise":[176],"ratio,":[177],"structural":[178],"similarity":[179],"subjective":[181],"performance.":[182]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}