iBet uBet
web content aggregator. Adding the entire web to your favor.
Link to original content:
https://api.openalex.org/works/doi:10.48550/ARXIV.2311.14760
{"id":"https://openalex.org/W4389115208","doi":"https://doi.org/10.48550/arxiv.2311.14760","title":"SinSR: Diffusion-Based Image Super-Resolution in a Single Step","display_name":"SinSR: Diffusion-Based Image Super-Resolution in a Single Step","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389115208","doi":"https://doi.org/10.48550/arxiv.2311.14760"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2311.14760","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2311.14760","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100374835","display_name":"Yufei Wang","orcid":"https://orcid.org/0000-0003-1365-6554"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yufei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070884682","display_name":"Wenhan Yang","orcid":"https://orcid.org/0000-0002-1692-0069"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wenhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664512","display_name":"Xinyuan Chen","orcid":"https://orcid.org/0000-0002-5517-7255"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xinyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100672233","display_name":"Yaohui Wang","orcid":"https://orcid.org/0009-0002-6262-7450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yaohui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101553481","display_name":"Lanqing Guo","orcid":"https://orcid.org/0000-0002-9452-4723"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Lanqing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044722301","display_name":"Lap\u2010Pui Chau","orcid":"https://orcid.org/0000-0003-4932-0593"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chau, Lap-Pui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406050","display_name":"Ziwei Liu","orcid":"https://orcid.org/0000-0002-4220-5958"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ziwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100748135","display_name":"Yu Qiao","orcid":"https://orcid.org/0000-0002-1889-2567"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080977911","display_name":"Alex C. Kot","orcid":"https://orcid.org/0000-0001-6262-8125"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kot, Alex C.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102557254","display_name":"Bihan Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Bihan","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":2,"citation_normalized_percentile":{"value":0.76594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":80,"max":85},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Single Image Super-Resolution Techniques","score":0.9977,"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/T11105","display_name":"Single Image Super-Resolution Techniques","score":0.9977,"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/T11304","display_name":"Diffusion Magnetic Resonance Imaging","score":0.9861,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Theory and Applications of Compressed Sensing","score":0.9644,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.723464},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6785984},{"id":"https://openalex.org/keywords/super-resolution","display_name":"Super-Resolution","score":0.604353},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse Approximation","score":0.54507},{"id":"https://openalex.org/keywords/diffusion-mri","display_name":"Diffusion MRI","score":0.540878},{"id":"https://openalex.org/keywords/sparse-representations","display_name":"Sparse Representations","score":0.524891},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed Sensing","score":0.5181}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.77521515},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.723464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7152432},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6785984},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.51559013},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.42391935},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.41995597},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41297376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30069083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2556842},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.21514359},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.094608486}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2311.14760","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2311.14760","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2311.14760","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.66}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W98480971","https://openalex.org/W3046370962","https://openalex.org/W2597809628","https://openalex.org/W2164382479","https://openalex.org/W2157978810","https://openalex.org/W2150291671","https://openalex.org/W2146343568","https://openalex.org/W2058965144","https://openalex.org/W2027972911","https://openalex.org/W2013643406"],"abstract_inverted_index":{"While":[0],"super-resolution":[1],"(SR)":[2],"methods":[3,25,215],"based":[4],"on":[5,43,193],"diffusion":[6],"models":[7],"exhibit":[8],"promising":[9],"results,":[10],"their":[11],"practical":[12],"application":[13],"is":[14,173],"hindered":[15],"by":[16,177],"the":[17,30,35,48,89,101,104,109,158,162,167,170,178,182,200,217],"substantial":[18],"number":[19,121],"of":[20,47,122,169,181],"required":[21],"inference":[22,64,123,146],"steps.":[23],"Recent":[24],"utilize":[26],"degraded":[27],"images":[28],"in":[29,116,186,220,226],"initial":[31],"state,":[32],"thereby":[33],"shortening":[34],"Markov":[36],"chain.":[37],"Nevertheless,":[38],"these":[39],"solutions":[40],"either":[41],"rely":[42],"a":[44,54,68,84,117,137,151,227],"precise":[45],"formulation":[46],"degradation":[49],"process":[50,87],"or":[51,206],"still":[52],"necessitate":[53],"relatively":[55],"lengthy":[56],"generation":[57],"path":[58],"(e.g.,":[59],"15":[60],"iterations).":[61],"To":[62],"enhance":[63],"speed,":[65],"we":[66,81,149],"propose":[67,150],"simple":[69],"yet":[70],"effective":[71],"method":[72,94,202],"for":[73,95,233],"achieving":[74],"single-step":[75],"SR":[76,142],"generation,":[77],"named":[78],"SinSR.":[79],"Specifically,":[80],"first":[82],"derive":[83],"deterministic":[85,131],"sampling":[86,223],"from":[88],"most":[90],"recent":[91],"state-of-the-art":[92],"(SOTA)":[93],"accelerating":[96],"diffusion-based":[97],"SR.":[98],"This":[99],"allows":[100],"mapping":[102,132],"between":[103],"input":[105],"random":[106],"noise":[107],"and":[108,119,195,216],"generated":[110],"high-resolution":[111],"image":[112,160],"to":[113,155,211,230],"be":[114,134,238],"obtained":[115],"reduced":[118],"acceptable":[120],"steps":[124],"during":[125,161],"training.":[126],"We":[127],"show":[128],"that":[129,140,166,199],"this":[130],"can":[133,203],"distilled":[135],"into":[136],"student":[138,171],"model":[139,172],"performs":[141],"within":[143],"only":[144],"one":[145,222],"step.":[147],"Additionally,":[148],"novel":[152],"consistency-preserving":[153],"loss":[154],"simultaneously":[156],"leverage":[157],"ground-truth":[159],"distillation":[163],"process,":[164],"ensuring":[165],"performance":[168,188,209],"not":[174],"solely":[175],"bound":[176],"feature":[179],"manifold":[180],"teacher":[183,218],"model,":[184,219],"resulting":[185,225],"further":[187],"improvement.":[189],"Extensive":[190],"experiments":[191],"conducted":[192],"synthetic":[194],"real-world":[196],"datasets":[197],"demonstrate":[198],"proposed":[201],"achieve":[204],"comparable":[205],"even":[207],"superior":[208],"compared":[210],"both":[212],"previous":[213],"SOTA":[214],"just":[221],"step,":[224],"remarkable":[228],"up":[229],"x10":[231],"speedup":[232],"inference.":[234],"Our":[235],"code":[236],"will":[237],"released":[239],"at":[240],"https://github.com/wyf0912/SinSR":[241]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4389115208","counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2024-11-30T23:02:50.545671","created_date":"2023-11-29"}
