{"id":"https://openalex.org/W4295690791","doi":"https://doi.org/10.48550/arxiv.2209.04747","title":"Diffusion Models in Vision: A Survey","display_name":"Diffusion Models in Vision: A Survey","publication_year":2022,"publication_date":"2022-09-10","ids":{"openalex":"https://openalex.org/W4295690791","doi":"https://doi.org/10.48550/arxiv.2209.04747"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2209.04747","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.04747","pdf_url":"https://arxiv.org/pdf/2209.04747","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.04747","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001612779","display_name":"Florinel-Alin Croitoru","orcid":"https://orcid.org/0009-0002-4187-4102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Croitoru, Florinel-Alin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079598088","display_name":"Vlad Hondru","orcid":"https://orcid.org/0009-0003-8074-6266"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hondru, Vlad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081017623","display_name":"Radu Tudor Ionescu","orcid":"https://orcid.org/0000-0002-9301-1950"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ionescu, Radu Tudor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103215307","display_name":"Mubarak Shah","orcid":"https://orcid.org/0000-0002-8216-1128"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Mubarak","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":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"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":0.9772999882698059,"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":0.9772999882698059,"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/T11829","display_name":"Mathematical Biology Tumor Growth","score":0.9211000204086304,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.906499981880188,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.685320258140564},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5700517892837524},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5629209280014038},{"id":"https://openalex.org/keywords/diffusion-process","display_name":"Diffusion process","score":0.5142309069633484},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4882941246032715},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.47831228375434875},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.47709447145462036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46087533235549927},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4240318238735199},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3821430206298828}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685320258140564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5700517892837524},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5629209280014038},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.5142309069633484},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4882941246032715},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.47831228375434875},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.47709447145462036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46087533235549927},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4240318238735199},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3821430206298828},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.0},{"id":"https://openalex.org/C3017618536","wikidata":"https://www.wikidata.org/wiki/Q304994","display_name":"Innovation diffusion","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2209.04747","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.04747","pdf_url":"https://arxiv.org/pdf/2209.04747","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2209.04747","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2209.04747","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2209.04747","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.04747","pdf_url":"https://arxiv.org/pdf/2209.04747","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.46000000834465027,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W2386796262","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W3174044702","https://openalex.org/W2967848559","https://openalex.org/W4299831724"],"abstract_inverted_index":{"Denoising":[0],"diffusion":[1,21,36,41,46,81,130,151,159,176,203,216],"models":[2,87,131,177,192,204,217],"represent":[3],"a":[4,24,34,39,65,123,199],"recent":[5],"emerging":[6],"topic":[7],"in":[8,14,133,141,206],"computer":[9,207],"vision,":[10,134],"demonstrating":[11],"remarkable":[12],"results":[13],"the":[15,44,48,62,71,80,92,97,110,142,173,212],"area":[16],"of":[17,96,113,126,202,215],"generative":[18,26,181,186],"modeling.":[19],"A":[20],"model":[22,27,66],"is":[23,29,51,67],"deep":[25,180],"that":[28],"based":[30,156],"on":[31,128,157],"two":[32],"stages,":[33],"forward":[35,45],"stage":[37],"and":[38,94,138,147,166,178,193,218],"reverse":[40,63,79],"stage.":[42],"In":[43,61,118],"stage,":[47,64],"input":[49,73],"data":[50,74],"gradually":[52,78],"perturbed":[53],"over":[54],"several":[55],"steps":[56,114],"by":[57,75,84],"adding":[58],"Gaussian":[59],"noise.":[60],"tasked":[68],"at":[69],"recovering":[70],"original":[72],"learning":[76],"to":[77,109],"process,":[82],"step":[83],"step.":[85],"Diffusion":[86],"are":[88,155],"widely":[89],"appreciated":[90],"for":[91,223],"quality":[93],"diversity":[95],"generated":[98],"samples,":[99],"despite":[100],"their":[101],"known":[102],"computational":[103],"burdens,":[104],"i.e.":[105],"low":[106],"speeds":[107],"due":[108],"high":[111],"number":[112],"involved":[115],"during":[116],"sampling.":[117],"this":[119],"survey,":[120],"we":[121,145,197,210],"provide":[122],"comprehensive":[124],"review":[125],"articles":[127],"denoising":[129,158],"applied":[132,205],"comprising":[135],"both":[136],"theoretical":[137],"practical":[139],"contributions":[140],"field.":[143],"First,":[144],"identify":[146],"present":[148],"three":[149],"generic":[150],"modeling":[152],"frameworks,":[153],"which":[154],"probabilistic":[160],"models,":[161,182,190],"noise":[162],"conditioned":[163],"score":[164],"networks,":[165,188],"stochastic":[167],"differential":[168],"equations.":[169],"We":[170],"further":[171],"discuss":[172],"relations":[174],"between":[175],"other":[179],"including":[183],"variational":[184],"auto-encoders,":[185],"adversarial":[187],"energy-based":[189],"autoregressive":[191],"normalizing":[194],"flows.":[195],"Then,":[196],"introduce":[198],"multi-perspective":[200],"categorization":[201],"vision.":[208],"Finally,":[209],"illustrate":[211],"current":[213],"limitations":[214],"envision":[219],"some":[220],"interesting":[221],"directions":[222],"future":[224],"research.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-14T07:06:38.338062","created_date":"2025-10-10T00:00:00"}
