{"id":"https://openalex.org/W4385488396","doi":"https://doi.org/10.54097/fcis.v4i2.9761","title":"A Lightweight Dual-Branch Image Dehazing Network based on Associative Learning","display_name":"A Lightweight Dual-Branch Image Dehazing Network based on Associative Learning","publication_year":2023,"publication_date":"2023-06-25","ids":{"openalex":"https://openalex.org/W4385488396","doi":"https://doi.org/10.54097/fcis.v4i2.9761"},"language":"en","primary_location":{"id":"doi:10.54097/fcis.v4i2.9761","doi":"https://doi.org/10.54097/fcis.v4i2.9761","is_oa":true,"landing_page_url":"http://dx.doi.org/10.54097/fcis.v4i2.9761","pdf_url":"https://drpress.org/ojs/index.php/fcis/article/download/9761/9496","source":{"id":"https://openalex.org/S4387283309","display_name":"Frontiers in Computing and Intelligent Systems","issn_l":"2832-6024","issn":["2832-6024"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computing and Intelligent Systems","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W4385488396"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://drpress.org/ojs/index.php/fcis/article/download/9761/9496","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaoxiao Xu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoxiao Xu","raw_affiliation_strings":["College of Computer Science and Technology, Qingdao University, Qingdao Shandong, 266000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Qingdao University, Qingdao Shandong, 266000, China","institution_ids":["https://openalex.org/I108688024"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108688024"],"apc_list":null,"apc_paid":null,"fwci":0.0737,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.23916895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"4","issue":"2","first_page":"27","last_page":"30"},"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.0,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.0,"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/image-dehazing","display_name":"image dehazing","score":0.9990000128746033},{"id":"https://openalex.org/keywords/associative-learning","display_name":"associative learning","score":0.8999999761581421},{"id":"https://openalex.org/keywords/lightweight-neural-networks","display_name":"lightweight neural networks","score":0.7879999876022339},{"id":"https://openalex.org/keywords/dual-branch-network","display_name":"dual-branch network","score":0.9779999852180481},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"image enhancement","score":0.6970000267028809},{"id":"https://openalex.org/keywords/weight-sharing","display_name":"parameter sharing","score":0.9909999966621399},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5870000123977661}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8340039253234863},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.8151311874389648},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.610236406326294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5982723236083984},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.49538910388946533},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49385982751846313},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4491061866283417},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4455917477607727},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.44522589445114136},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4434669613838196},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07598415017127991},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07164040207862854},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.54097/fcis.v4i2.9761","doi":"https://doi.org/10.54097/fcis.v4i2.9761","is_oa":true,"landing_page_url":"http://dx.doi.org/10.54097/fcis.v4i2.9761","pdf_url":"https://drpress.org/ojs/index.php/fcis/article/download/9761/9496","source":{"id":"https://openalex.org/S4387283309","display_name":"Frontiers in Computing and Intelligent Systems","issn_l":"2832-6024","issn":["2832-6024"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computing and Intelligent Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.54097/fcis.v4i2.9761","doi":"https://doi.org/10.54097/fcis.v4i2.9761","is_oa":true,"landing_page_url":"http://dx.doi.org/10.54097/fcis.v4i2.9761","pdf_url":"https://drpress.org/ojs/index.php/fcis/article/download/9761/9496","source":{"id":"https://openalex.org/S4387283309","display_name":"Frontiers in Computing and Intelligent Systems","issn_l":"2832-6024","issn":["2832-6024"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computing and Intelligent Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385488396.pdf","grobid_xml":"https://content.openalex.org/works/W4385488396.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W2057321661","https://openalex.org/W2114867966","https://openalex.org/W2124500511","https://openalex.org/W2128254161","https://openalex.org/W2128926607","https://openalex.org/W2133665775","https://openalex.org/W2157233332","https://openalex.org/W2164847484","https://openalex.org/W2166802795","https://openalex.org/W2169447236","https://openalex.org/W2256362396","https://openalex.org/W2325709085","https://openalex.org/W2519481857","https://openalex.org/W2607041014","https://openalex.org/W2779176852","https://openalex.org/W2796555273","https://openalex.org/W2962782447","https://openalex.org/W2963152299","https://openalex.org/W2963928582","https://openalex.org/W2998249728","https://openalex.org/W3021381664","https://openalex.org/W3190499751"],"related_works":["https://openalex.org/W4385802714","https://openalex.org/W4391650992","https://openalex.org/W4392559939","https://openalex.org/W4396871852","https://openalex.org/W4410153875","https://openalex.org/W4402859309","https://openalex.org/W7138836218","https://openalex.org/W2964509837","https://openalex.org/W4408077194","https://openalex.org/W4410395185"],"abstract_inverted_index":{"Haze":[0],"degrades":[1],"the":[2,51,63,103,133],"clarity,":[3],"contrast,":[4],"and":[5,24,40,90,100,129,141],"details":[6],"of":[7,54,84,135],"images,":[8],"resulting":[9],"in":[10,13,45,138],"a":[11,19,70,85,91],"decrease":[12],"image":[14,27,64,73,93,157],"quality.":[15],"Image":[16],"dehazing":[17,65,74,87,158],"provides":[18],"means":[20],"to":[21,145],"obtain":[22],"clearer":[23],"more":[25],"accurate":[26],"information.":[28],"Traditional":[29],"methods":[30],"for":[31,111,155],"haze":[32],"removal":[33],"typically":[34],"rely":[35],"on":[36,77,127],"manually":[37],"designed":[38],"features":[39,122],"models,":[41],"limiting":[42],"their":[43,120],"performance":[44],"complex":[46],"scenes.":[47],"In":[48],"recent":[49],"years,":[50],"rapid":[52],"advancement":[53],"deep":[55],"learning":[56,79],"has":[57],"offered":[58],"new":[59],"insights":[60],"into":[61],"addressing":[62],"problem.":[66],"This":[67],"paper":[68],"proposes":[69],"lightweight":[71,86,92],"dual-branch":[72],"network":[75,82],"based":[76],"associative":[78,118],"(LDANet).":[80],"The":[81],"consists":[83],"sub-network":[88],"(LDSN)":[89],"enhancement":[94],"subnetwork":[95],"(LESN).":[96],"To":[97],"reduce":[98],"computational":[99],"parameter":[101,112],"complexity,":[102],"Tied":[104],"Block":[105],"Convolution":[106],"(TBC)":[107],"is":[108],"employed,":[109],"allowing":[110],"sharing":[113],"among":[114],"components.":[115],"Lastly,":[116],"through":[117],"learning,":[119],"distinctive":[121],"are":[123],"mapped.":[124],"Extensive":[125],"experiments":[126],"synthetic":[128],"real-world":[130,156],"datasets":[131],"demonstrate":[132],"superiority":[134],"our":[136],"approach":[137],"qualitative":[139],"comparisons":[140],"quantitative":[142],"evaluations":[143],"compared":[144],"other":[146],"state-of-the-art":[147],"methods.":[148],"Our":[149],"method":[150],"holds":[151],"significant":[152],"practical":[153],"value":[154],"scenarios.":[159]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2023-08-03T00:00:00"}
