{"id":"https://openalex.org/W4392903676","doi":"https://doi.org/10.1109/icassp48485.2024.10448036","title":"Multi-Weather Degradation-Aware Transformer for Image Restoration","display_name":"Multi-Weather Degradation-Aware Transformer for Image Restoration","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392903676","doi":"https://doi.org/10.1109/icassp48485.2024.10448036"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10448036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 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/A5101233722","display_name":"Ruoxi Zhu","orcid":"https://orcid.org/0009-0004-2017-6705"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoxi Zhu","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110791673","display_name":"Minfeng Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210099446","display_name":"Huadong Hospital","ror":"https://ror.org/012wm7481","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minfeng Wu","raw_affiliation_strings":["Huadong Hospital Affiliated to Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huadong Hospital Affiliated to Fudan University","institution_ids":["https://openalex.org/I24943067","https://openalex.org/I4210099446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112620081","display_name":"Xiankui Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I75746372","display_name":"ZTE (United States)","ror":"https://ror.org/0518yg160","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098582","https://openalex.org/I75746372"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiankui Xiong","raw_affiliation_strings":["ZTE Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZTE Corporation","institution_ids":["https://openalex.org/I75746372"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062059596","display_name":"Xuanpeng Zhu","orcid":"https://orcid.org/0009-0000-4859-9604"},"institutions":[{"id":"https://openalex.org/I75746372","display_name":"ZTE (United States)","ror":"https://ror.org/0518yg160","country_code":"US","type":"company","lineage":["https://openalex.org/I4210098582","https://openalex.org/I75746372"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuanpeng Zhu","raw_affiliation_strings":["ZTE Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ZTE Corporation","institution_ids":["https://openalex.org/I75746372"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085004179","display_name":"Yibo Fan","orcid":"https://orcid.org/0000-0003-2523-8261"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibo Fan","raw_affiliation_strings":["Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2629,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46285759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3765","last_page":"3769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9993000030517578,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.998199999332428,"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":0.7225595116615295},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6824249625205994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5303666591644287},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.508611798286438},{"id":"https://openalex.org/keywords/adverse-weather","display_name":"Adverse weather","score":0.4703029990196228},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4265408515930176},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.4207102358341217},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37888163328170776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3576834201812744},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26647302508354187},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1595247983932495},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.15029937028884888},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.08309856057167053},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.07931345701217651}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7225595116615295},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6824249625205994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303666591644287},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.508611798286438},{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.4703029990196228},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4265408515930176},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.4207102358341217},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37888163328170776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3576834201812744},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26647302508354187},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1595247983932495},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.15029937028884888},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.08309856057167053},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.07931345701217651},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10448036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2734498959","https://openalex.org/W2748263833","https://openalex.org/W2884068670","https://openalex.org/W2963800716","https://openalex.org/W2963866045","https://openalex.org/W2967584026","https://openalex.org/W3035713416","https://openalex.org/W3104533206","https://openalex.org/W3133769291","https://openalex.org/W3168684807","https://openalex.org/W3194523157","https://openalex.org/W3211490618","https://openalex.org/W3215632849","https://openalex.org/W4312373367","https://openalex.org/W4312678820","https://openalex.org/W4313192952","https://openalex.org/W4317496742","https://openalex.org/W4372260536","https://openalex.org/W6740674931","https://openalex.org/W6797399245"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Restoring":[0],"images":[1,74,135],"under":[2],"different":[3,77,137],"adverse":[4,78],"weather":[5,18,40,55],"conditions":[6],"with":[7,80,136,155,158],"a":[8,26,49,58,64],"single":[9,59],"model":[10,87,150],"is":[11,32,42],"practical":[12],"in":[13,35],"many":[14],"applications.":[15],"Most":[16],"existing":[17],"restoration":[19,113],"approaches":[20],"are":[21],"only":[22],"able":[23],"to":[24,52,94],"handle":[25],"specific":[27],"type":[28,41],"of":[29,84,100],"degradation,":[30],"which":[31],"often":[33],"insufficient":[34],"real-world":[36],"scenarios":[37],"where":[38],"the":[39,81,111,144],"unknown.":[43],"In":[44],"this":[45],"paper,":[46],"we":[47,62,89],"propose":[48],"holistic":[50],"solution":[51],"solve":[53],"multiple":[54],"degradations":[56],"using":[57,120],"model.":[60],"Specifically,":[61],"build":[63],"weather-type":[65],"aware":[66],"Transformer,":[67],"an":[68,96],"efficient":[69],"architecture":[70],"that":[71,148],"can":[72,115],"restore":[73],"degraded":[75],"by":[76,107],"weathers":[79],"same":[82],"set":[83],"parameters.":[85],"For":[86],"training,":[88],"first":[90],"use":[91],"contrastive":[92],"loss":[93],"train":[95],"auxiliary":[97],"hypernetwork":[98],"capable":[99],"extracting":[101],"content-independent,":[102],"distortion-aware":[103],"feature":[104],"embeddings.":[105],"Guided":[106],"these":[108],"weather-dependent":[109],"features,":[110],"image":[112],"Transformer":[114],"adaptively":[116,133],"modulate":[117],"its":[118],"parameters":[119],"hypernetworks":[121],"and":[122,130,140],"feature-wise":[123],"linear":[124],"modulation":[125],"blocks,":[126],"conducting":[127],"both":[128],"local":[129],"global":[131],"operations":[132],"for":[134],"degradations.":[138],"Qualitative":[139],"quantitative":[141],"results":[142],"on":[143],"multi-weather":[145],"benchmark":[146],"demonstrate":[147],"our":[149],"achieves":[151],"significant":[152],"improvements":[153],"compared":[154],"previous":[156],"state-of-the-arts,":[157],"even":[159],"less":[160],"computational":[161],"cost.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}