{"id":"https://openalex.org/W3160822809","doi":"https://doi.org/10.1109/icassp39728.2021.9414179","title":"DNANet: Dense Nested Attention Network for Single Image Dehazing","display_name":"DNANet: Dense Nested Attention Network for Single Image Dehazing","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3160822809","doi":"https://doi.org/10.1109/icassp39728.2021.9414179","mag":"3160822809"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5045967449","display_name":"Dongdong Ren","orcid":"https://orcid.org/0000-0002-2889-9375"},"institutions":[{"id":"https://openalex.org/I1300757298","display_name":"Heilongjiang University of Science and Technology","ror":"https://ror.org/030xwyx96","country_code":"CN","type":"education","lineage":["https://openalex.org/I1300757298"]},{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongdong Ren","raw_affiliation_strings":["School of Computer Science and Technology, Heilongjiang University, Harbin, China","Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Heilongjiang University, Harbin, China","institution_ids":["https://openalex.org/I1300757298"]},{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan, China","institution_ids":["https://openalex.org/I152269853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100665543","display_name":"Jinbao Li","orcid":"https://orcid.org/0000-0002-8478-3983"},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbao Li","raw_affiliation_strings":["Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan, China","institution_ids":["https://openalex.org/I152269853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100785901","display_name":"Meng Han","orcid":"https://orcid.org/0000-0001-7472-0842"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meng Han","raw_affiliation_strings":["Data-driven Intelligence Research (DIR) Lab, Kennesaw State University, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data-driven Intelligence Research (DIR) Lab, Kennesaw State University, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044470103","display_name":"Minglei Shu","orcid":"https://orcid.org/0000-0002-7136-1538"},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglei Shu","raw_affiliation_strings":["Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Qilu University of Technology, Jinan, China","institution_ids":["https://openalex.org/I152269853"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2035","last_page":"2039"},"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":0.9987000226974487,"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":0.9976999759674072,"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":0.792054295539856},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7353167533874512},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6945632696151733},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6150469779968262},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5689811706542969},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5520289540290833},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5425089597702026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5112743377685547},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.4841197431087494},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47474032640457153},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4745232164859772},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.45462632179260254},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.42248696088790894},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3870980441570282},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35493338108062744},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32382601499557495},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23660114407539368},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2351107895374298},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08806127309799194}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.792054295539856},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7353167533874512},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6945632696151733},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6150469779968262},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5689811706542969},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5520289540290833},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5425089597702026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5112743377685547},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.4841197431087494},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47474032640457153},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4745232164859772},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.45462632179260254},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.42248696088790894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3870980441570282},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35493338108062744},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32382601499557495},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23660114407539368},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2351107895374298},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08806127309799194},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1901129140","https://openalex.org/W1990592195","https://openalex.org/W2194775991","https://openalex.org/W2256362396","https://openalex.org/W2752782242","https://openalex.org/W2779176852","https://openalex.org/W2792829624","https://openalex.org/W2799213142","https://openalex.org/W2884585870","https://openalex.org/W2895176907","https://openalex.org/W2948606054","https://openalex.org/W2962754725","https://openalex.org/W2963306157","https://openalex.org/W2963446712","https://openalex.org/W2963928582","https://openalex.org/W2969134192","https://openalex.org/W2985030998","https://openalex.org/W2990007814","https://openalex.org/W2998249728","https://openalex.org/W3034278302","https://openalex.org/W6639824700","https://openalex.org/W6647720530","https://openalex.org/W6753412334"],"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":{"In":[0],"this":[1,111],"paper,":[2],"we":[3,85,113,138],"propose":[4],"an":[5,140],"innovative":[6],"approach,":[7],"called":[8],"Dense":[9],"Nested":[10],"Attention":[11],"Network":[12],"(DNANet),":[13],"to":[14,38,51,60,73,102,118,152],"directly":[15],"restore":[16],"a":[17,21,25,61,166],"clear":[18],"image":[19,23],"from":[20,36,49,99],"hazy":[22],"with":[24],"new":[26],"topology":[27],"of":[28,77,89,96,123,147],"connection":[29],"paths.":[30],"Firstly,":[31],"through":[32],"dense":[33],"nested":[34,150],"connections":[35,91],"inside":[37],"outside,":[39],"the":[40,55,70,75,78,87,94,100,106,115,121,128,132,148,170],"DNANet":[41,161],"can":[42],"fuse":[43],"both":[44],"shallow":[45,101],"and":[46,58],"deep":[47,103],"features":[48],"fine":[50],"coarse,":[52],"then":[53],"strengthen":[54],"feature":[56,135],"propagation":[57],"reuse":[59],"large":[62,167],"extent.":[63],"We":[64],"use":[65,114],"stacked":[66],"dilated":[67],"convolutions,":[68],"as":[69],"basic":[71],"operation,":[72],"alleviate":[74],"shortcomings":[76],"traditional":[79],"context":[80],"information":[81,129],"aggregation":[82],"methods.":[83],"Secondly,":[84],"examine":[86],"weakness":[88],"skipping":[90],"by":[92,126,165],"reasoning":[93],"existence":[95],"residual":[97,124],"haze":[98,125],"layers":[104],"in":[105,173],"neural":[107],"network.":[108],"To":[109],"address":[110],"problem,":[112],"attention":[116],"mechanism":[117],"filter":[119],"out":[120],"output":[122],"capturing":[127],"relations":[130],"on":[131,144,169],"entire":[133],"skip":[134],"maps.":[136],"Thirdly,":[137],"introduce":[139],"adjustable":[141],"loss":[142],"constraint":[143],"each":[145],"block":[146],"outermost":[149],"structure":[151],"gather":[153],"more":[154],"accurate":[155],"features.":[156],"The":[157],"result":[158],"demonstrates":[159],"that":[160],"outperforms":[162],"state-of-the-art":[163],"methods":[164],"margin":[168],"benchmark":[171],"datasets":[172],"extensive":[174],"experiments.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
