{"id":"https://openalex.org/W7171983243","doi":"https://doi.org/10.1145/3774521.3774565","title":"A Unified model for the Adverse Weather Removal using Squeeze-and-Excitation Attention","display_name":"A Unified model for the Adverse Weather Removal using Squeeze-and-Excitation Attention","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7171983243","doi":"https://doi.org/10.1145/3774521.3774565"},"language":null,"primary_location":{"id":"doi:10.1145/3774521.3774565","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774565","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774521.3774565","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5144159861","display_name":"Anuran Basu","orcid":"https://orcid.org/0009-0002-1144-0557"},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anuran Basu","raw_affiliation_strings":["Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India"],"raw_orcid":"https://orcid.org/0009-0002-1144-0557","affiliations":[{"raw_affiliation_string":"Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5144149416","display_name":"Kotha Kartheek","orcid":"https://orcid.org/0009-0008-6472-8566"},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kotha Kartheek","raw_affiliation_strings":["Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India"],"raw_orcid":"https://orcid.org/0009-0008-6472-8566","affiliations":[{"raw_affiliation_string":"Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120366705","display_name":"Lingamaneni Gnanesh Chowdary","orcid":null},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lingamaneni Gnanesh Chowdary","raw_affiliation_strings":["Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India"],"raw_orcid":"https://orcid.org/0009-0007-7306-3642","affiliations":[{"raw_affiliation_string":"Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070435685","display_name":"Snehasis Mukherjee","orcid":"https://orcid.org/0000-0002-2196-8980"},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Snehasis Mukherjee","raw_affiliation_strings":["Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India"],"raw_orcid":"https://orcid.org/0000-0002-2196-8980","affiliations":[{"raw_affiliation_string":"Shiv Nadar Institution of Eminence, Delhi NCR, Delhi NCR, India","institution_ids":["https://openalex.org/I26604189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I26604189"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6333000063896179},{"id":"https://openalex.org/keywords/unified-model","display_name":"Unified Model","score":0.580299973487854},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5460000038146973},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5234000086784363},{"id":"https://openalex.org/keywords/adverse-weather","display_name":"Adverse weather","score":0.47839999198913574},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47209998965263367},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41130000352859497},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.382999986410141}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7321000099182129},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6333000063896179},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.580299973487854},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5113000273704529},{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.47839999198913574},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44519999623298645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.35659998655319214},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33799999952316284},{"id":"https://openalex.org/C205537798","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Extreme weather","level":3,"score":0.3361000120639801},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C2993843871","wikidata":"https://www.wikidata.org/wiki/Q11663","display_name":"Hot weather","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26460000872612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774521.3774565","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774565","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774521.3774565","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774565","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W2128254161","https://openalex.org/W2256362396","https://openalex.org/W2466666260","https://openalex.org/W2559264300","https://openalex.org/W2748263833","https://openalex.org/W2752782242","https://openalex.org/W2779176852","https://openalex.org/W2798401637","https://openalex.org/W2884068670","https://openalex.org/W2884585870","https://openalex.org/W2912435603","https://openalex.org/W2963074253","https://openalex.org/W2963878020","https://openalex.org/W2998249728","https://openalex.org/W3012002540","https://openalex.org/W3035713416","https://openalex.org/W3103549414","https://openalex.org/W3116616514","https://openalex.org/W3127896596","https://openalex.org/W3173269149","https://openalex.org/W3194523157","https://openalex.org/W3201952986","https://openalex.org/W3215632849","https://openalex.org/W4213091815","https://openalex.org/W4221166948","https://openalex.org/W4225672218","https://openalex.org/W4226134400","https://openalex.org/W4309928010","https://openalex.org/W4310818411","https://openalex.org/W4312625691","https://openalex.org/W4312632902","https://openalex.org/W4372270072","https://openalex.org/W4386072291","https://openalex.org/W4387967535","https://openalex.org/W4391697048","https://openalex.org/W4393150006","https://openalex.org/W4402351638","https://openalex.org/W4404876506","https://openalex.org/W4406981385"],"related_works":[],"abstract_inverted_index":{"Adverse":[0],"weather":[1,49,65,107],"conditions":[2,24],"such":[3,53],"as":[4,54,92,94],"haze,":[5],"rain,":[6],"and":[7,31,132,158,166],"snow":[8],"present":[9],"in":[10,20,164],"images,":[11],"pose":[12],"substantial":[13],"challenges":[14],"to":[15,34,43,71,87],"computer":[16],"vision":[17],"systems":[18],"operating":[19],"real-world":[21,51],"environments.":[22],"These":[23],"obscure":[25],"scene":[26],"details,":[27],"distort":[28],"critical":[29],"features,":[30],"often":[32],"lead":[33],"unreliable":[35],"predictions.":[36],"While":[37],"most":[38],"existing":[39],"methods":[40],"are":[41],"tailored":[42],"address":[44],"a":[45,58,69,77,113,129,143],"particular":[46],"type":[47],"of":[48,62,146],"degradation,":[50],"applications":[52],"autonomous":[55],"driving":[56],"demand":[57],"unified":[59,115],"solution":[60],"capable":[61],"handling":[63],"multiple":[64],"scenarios.":[66],"We":[67],"propose":[68],"framework":[70],"integrate":[72],"Squeeze-and-Excitation":[73],"(SE)":[74],"module":[75],"into":[76,105],"CycleGAN-based":[78],"architecture":[79],"trained":[80],"with":[81],"contrastive":[82],"loss,":[83],"enabling":[84],"the":[85,136,169],"network":[86],"adaptively":[88],"emphasize":[89],"condition-specific":[90],"features":[91],"well":[93],"enhance":[95],"feature":[96],"discrimination":[97],"while":[98,121],"preserving":[99],"semantic":[100],"consistency":[101],"during":[102],"domain":[103],"translation":[104],"different":[106],"conditions.":[108,149],"The":[109,171],"proposed":[110],"approach":[111],"maintains":[112],"single":[114],"model,":[116],"significantly":[117],"reducing":[118],"model":[119],"complexity":[120],"improving":[122],"generalization":[123],"across":[124],"heterogeneous":[125],"weather-induced":[126],"degradations.":[127],"Through":[128],"lightweight,":[130],"interpretable,":[131],"efficient":[133],"attention":[134],"mechanism,":[135],"method":[137],"achieves":[138],"robust":[139],"image":[140],"restoration":[141],"under":[142],"wide":[144],"range":[145],"adverse":[147],"environmental":[148],"Extensive":[150],"experiments":[151],"on":[152],"benchmark":[153],"datasets":[154],"for":[155],"dehazing,":[156],"desnowing":[157],"deraining":[159],"tasks":[160],"demonstrate":[161],"significant":[162],"improvements":[163],"PSNR":[165],"SSIM":[167],"over":[168],"state-of-the-art.":[170],"code":[172],"is":[173],"available":[174],"at":[175],"https://github.com/kartheekkotha/DA-AGLC-GAN-SE.":[176]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-08-01T00:00:00"}