{"id":"https://openalex.org/W4413146111","doi":"https://doi.org/10.1109/cvpr52734.2025.00227","title":"Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model","display_name":"Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model","publication_year":2025,"publication_date":"2025-06-10","ids":{"openalex":"https://openalex.org/W4413146111","doi":"https://doi.org/10.1109/cvpr52734.2025.00227"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52734.2025.00227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.00227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","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/A5100360653","display_name":"Yuhan Wang","orcid":"https://orcid.org/0000-0002-2942-5948"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuhan Wang","raw_affiliation_strings":["The Chinese University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021492036","display_name":"Suzhi Bi","orcid":"https://orcid.org/0000-0001-6212-690X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suzhi Bi","raw_affiliation_strings":["Shenzhen University"],"affiliations":[{"raw_affiliation_string":"Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004874287","display_name":"Ying\u2013Jun Angela Zhang","orcid":"https://orcid.org/0000-0002-7304-6849"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying-Jun Angela Zhang","raw_affiliation_strings":["The Chinese University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063946480","display_name":"Xiaojun Yuan","orcid":"https://orcid.org/0000-0002-0433-6535"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Yuan","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100360653"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2386318,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2377","last_page":"2386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9434999823570251,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9434999823570251,"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/T10320","display_name":"Neural Networks and Applications","score":0.9248999953269958,"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/traverse","display_name":"Traverse","score":0.8444334268569946},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.6918680667877197},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6395676136016846},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5475257635116577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5065456628799438},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4689449965953827},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42785096168518066},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3607267737388611},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3541719317436218},{"id":"https://openalex.org/keywords/geodesy","display_name":"Geodesy","score":0.10852634906768799},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09953433275222778},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08790507912635803},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06671637296676636}],"concepts":[{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.8444334268569946},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.6918680667877197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6395676136016846},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5475257635116577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5065456628799438},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4689449965953827},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42785096168518066},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3607267737388611},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3541719317436218},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.10852634906768799},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09953433275222778},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08790507912635803},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06671637296676636},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52734.2025.00227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.00227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4000000059604645,"display_name":"Decent work and economic growth"}],"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/W4391584540","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559"],"abstract_inverted_index":{"The":[0],"distortion-perception":[1],"(DP)":[2],"tradeoff":[3,131,156],"reveals":[4],"a":[5,97,105,145],"fundamental":[6],"conflict":[7],"between":[8],"distortion":[9],"metrics":[10],"(e.g.,":[11],"MSE":[12,46],"and":[13,15,91,110,140,151],"PSNR)":[14],"perceptual":[16,36],"quality.":[17],"Recent":[18],"research":[19],"has":[20],"increasingly":[21],"concentrated":[22],"on":[23,44,62,138],"evaluating":[24],"denoising":[25,159],"algorithms":[26,33],"within":[27],"the":[28,51,63,71,87,113,120,129,154],"DP":[29,64,94,130,155],"framework.":[30],"However,":[31],"existing":[32],"either":[34],"prioritize":[35],"quality":[37],"by":[38,74],"sacrificing":[39],"acceptable":[40],"distortion,":[41],"or":[42,54,68],"focus":[43],"minimizing":[45],"for":[47,132,157],"faithful":[48],"restoration.":[49],"When":[50],"goal":[52],"shifts":[53],"noisy":[55],"measurements":[56],"vary,":[57],"adapting":[58],"to":[59,128],"different":[60],"points":[61],"plane":[65],"needs":[66],"retraining":[67],"even":[69],"re-designing":[70],"model.":[72,101],"Inspired":[73],"recent":[75],"advances":[76],"in":[77],"solving":[78],"inverse":[79],"problems":[80],"using":[81,96],"score-based":[82,100],"generative":[83],"models,":[84],"we":[85,103],"explore":[86],"potential":[88],"of":[89],"flexibly":[90,152],"optimally":[92],"traversing":[93],"tradeoffs":[95],"single":[98,146],"pre-trained":[99],"Specifically,":[102],"introduce":[104],"variance-scaled":[106],"reverse":[107],"diffusion":[108],"process":[109,123],"theoretically":[111],"characterize":[112],"marginal":[114],"distribution.":[115,135],"We":[116],"then":[117],"prove":[118],"that":[119,144],"proposed":[121],"sample":[122],"is":[124],"an":[125],"optimal":[126],"solution":[127],"conditional":[133],"Gaussian":[134],"Experimental":[136],"results":[137],"two-dimensional":[139],"image":[141],"datasets":[142],"illustrate":[143],"score":[147],"network":[148],"can":[149],"effectively":[150],"traverse":[153],"general":[158],"problems.":[160]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
