{"id":"https://openalex.org/W7134075607","doi":"https://doi.org/10.48550/arxiv.2603.04870","title":"Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning","display_name":"Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134075607","doi":"https://doi.org/10.48550/arxiv.2603.04870"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04870","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004950146","display_name":"Jaekyun Ko","orcid":"https://orcid.org/0000-0002-6806-5885"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ko, Jaekyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121115883","display_name":"Dongjin Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Dongjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128255343","display_name":"Soomin Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Soomin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128225462","display_name":"Guanghui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Guanghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100438996","display_name":"Tae Hyun Kim","orcid":"https://orcid.org/0000-0002-7995-3984"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Tae Hyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28868226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.5982999801635742,"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":0.5982999801635742,"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":0.17100000381469727,"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/T11448","display_name":"Face recognition and analysis","score":0.020899999886751175,"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","display_name":"Noise (video)","score":0.6804999709129333},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49540001153945923},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.49390000104904175},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.46950000524520874},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44279998540878296},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.43849998712539673},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.3912999927997589}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7829999923706055},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6804999709129333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6610999703407288},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5622000098228455},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.46950000524520874},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44279998540878296},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3864000141620636},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.34880000352859497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28029999136924744},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.26660001277923584}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04870","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.04870","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04870","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.04870","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Denoising":[0],"in":[1,20,169],"the":[2,26,75,108,124,127,132,142,162],"sRGB":[3],"image":[4,31],"space":[5],"is":[6,23,99],"challenging":[7],"due":[8],"to":[9,38,50,70],"large":[10],"noise":[11,113,147,172],"variability.":[12],"Although":[13],"end-to-end":[14],"methods":[15,46],"perform":[16],"well,":[17],"their":[18,84],"effectiveness":[19],"real-world":[21,72,111,171],"scenarios":[22],"limited":[24,56],"by":[25],"scarcity":[27],"of":[28,77,101,110,118,126,146,165],"real":[29],"noisy-clean":[30],"pairs,":[32],"which":[33],"are":[34],"expensive":[35],"and":[36,68,114,144,160],"difficult":[37],"collect.":[39],"To":[40],"address":[41],"this":[42],"limitation,":[43],"several":[44],"generative":[45],"have":[47],"been":[48],"developed":[49],"synthesize":[51,71],"realistic":[52,119,157],"noisy":[53,120,158],"images":[54,121,159,168],"from":[55],"data.":[57],"These":[58],"approaches":[59],"often":[60],"rely":[61],"on":[62,134],"camera":[63,136],"metadata":[64,78],"during":[65],"both":[66],"training":[67],"testing":[69],"noise.":[73,129],"However,":[74],"lack":[76],"or":[79],"inconsistencies":[80],"between":[81],"devices":[82],"restricts":[83],"usability.":[85],"Therefore,":[86],"we":[87],"propose":[88],"a":[89,116],"novel":[90],"framework":[91],"called":[92],"Prompt-Driven":[93],"Noise":[94],"Generation":[95],"(PNG).":[96],"This":[97],"model":[98,154],"capable":[100],"acquiring":[102],"high-dimensional":[103],"prompt":[104],"features":[105],"that":[106,152],"capture":[107],"characteristics":[109],"input":[112,128],"creating":[115],"variety":[117],"consistent":[122],"with":[123],"distribution":[125],"By":[130],"eliminating":[131],"dependency":[133],"explicit":[135],"metadata,":[137],"our":[138,153],"approach":[139],"significantly":[140],"enhances":[141],"generalizability":[143],"applicability":[145],"synthesis.":[148],"Comprehensive":[149],"experiments":[150],"reveal":[151],"effectively":[155],"produces":[156],"show":[161],"successful":[163],"application":[164],"these":[166],"generated":[167],"removing":[170],"across":[173],"various":[174],"benchmark":[175],"datasets.":[176]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-07T00:00:00"}
