{"id":"https://openalex.org/W7137899702","doi":"https://doi.org/10.1609/aaai.v40i7.37483","title":"GuidNoise: Single-Pair Guided Diffusion for Generalized Noise Synthesis","display_name":"GuidNoise: Single-Pair Guided Diffusion for Generalized Noise Synthesis","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137899702","doi":"https://doi.org/10.1609/aaai.v40i7.37483"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i7.37483","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i7.37483","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i7.37483","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Changjin Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104271","display_name":"National Insurance Institute of Israel","ror":"https://ror.org/01fz2q334","country_code":"IL","type":"government","lineage":["https://openalex.org/I4210104271"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Changjin Kim","raw_affiliation_strings":["SNUAILAB"],"affiliations":[{"raw_affiliation_string":"SNUAILAB","institution_ids":["https://openalex.org/I4210104271"]}]},{"author_position":"middle","author":{"id":null,"display_name":"HyeokJun Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"HyeokJun Lee","raw_affiliation_strings":["Chung-Ang University"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":null,"display_name":"YoungJoon Yoo","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"YoungJoon Yoo","raw_affiliation_strings":["SNUAILAB\nChung-Ang University"],"affiliations":[{"raw_affiliation_string":"SNUAILAB\nChung-Ang University","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210104271"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13432836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"7","first_page":"5639","last_page":"5647"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6771000027656555,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6771000027656555,"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":0.2167000025510788,"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/T11019","display_name":"Image Enhancement Techniques","score":0.007199999876320362,"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/noise-reduction","display_name":"Noise reduction","score":0.6347000002861023},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5978999733924866},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5440000295639038},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.5223000049591064},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4853000044822693},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.4666999876499176},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4643999934196472},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4424000084400177},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.43849998712539673}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6884999871253967},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6347000002861023},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5978999733924866},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5210000276565552},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4853000044822693},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.4666999876499176},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4424000084400177},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.4004000127315521},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33239999413490295},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3285999894142151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.325300008058548},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2969000041484833},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.2711000144481659},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C50494287","wikidata":"https://www.wikidata.org/wiki/Q658467","display_name":"Texture synthesis","level":5,"score":0.2653000056743622},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.2567000091075897},{"id":"https://openalex.org/C2989087649","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Image synthesis","level":3,"score":0.2563999891281128},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i7.37483","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i7.37483","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i7.37483","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i7.37483","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4477105736732483}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"image":[1,33,153],"denoising":[2,172],"methods":[3],"have":[4],"leveraged":[5],"generative":[6,23],"modeling":[7],"for":[8,54,163],"real":[9],"noise":[10,56,125,135,160],"synthesis":[11],"to":[12,44,100],"address":[13],"the":[14,46,65,85,102,112,118,148],"costly":[15],"acquisition":[16],"of":[17,105,151],"real-world":[18],"noisy":[19,82,131],"data.":[20,167,184],"However,":[21],"these":[22],"models":[24,180],"typically":[25],"require":[26],"camera":[27],"metadata":[28,139],"and":[29,95,143,181],"extensive":[30],"target-specific":[31],"noisy-clean":[32,152],"pairs,":[34],"often":[35,67],"showing":[36],"limited":[37,182],"generalization":[38],"between":[39],"settings.":[40],"In":[41],"this":[42],"paper,":[43],"mitigate":[45],"prerequisites,":[47],"we":[48,87],"propose":[49],"a":[50,60,73,89,96],"Single-Pair":[51],"Guided":[52],"Diffusion":[53],"generalized":[55],"synthesis(GuidNoise),":[57],"which":[58,79],"uses":[59],"single":[61],"noisy/clean":[62],"pair":[63],"as":[64],"guidance,":[66,86],"easily":[68],"obtained":[69],"by":[70],"itself":[71],"within":[72],"training":[74,142,183],"set.":[75],"To":[76],"train":[77],"GuidNoise,":[78],"generates":[80],"synthetic":[81,159],"images":[83,132],"from":[84],"introduce":[88],"guidance-aware":[90],"affine":[91],"feature":[92],"modification":[93],"(GAFM)":[94],"noise-aware":[97],"refine":[98],"loss":[99,109],"leverage":[101],"inherent":[103],"potential":[104],"diffusion":[106,113],"models.":[107],"This":[108,168],"function":[110],"refines":[111],"model\u2019s":[114],"backward":[115],"process,":[116],"making":[117,158],"model":[119],"more":[120],"adept":[121],"at":[122,155],"generating":[123],"realistic":[124],"distributions.":[126],"The":[127],"GuidNoise":[128,146],"synthesizes":[129],"high-quality":[130],"under":[133],"diverse":[134],"environments":[136],"without":[137],"additional":[138],"during":[140],"both":[141],"inference.":[144],"Additionally,":[145],"enables":[147],"efficient":[149],"generation":[150],"pairs":[154],"inference":[156],"time,":[157],"readily":[161],"applicable":[162],"augmenting":[164],"train-":[165],"ing":[166],"self-augmentation":[169],"significantly":[170],"improves":[171],"performance,":[173],"especially":[174],"in":[175],"practical":[176],"scenarios":[177],"with":[178],"lightweight":[179]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-02-06T00:00:00"}