{"id":"https://openalex.org/W3083244333","doi":"https://doi.org/10.1109/tip.2020.3020389","title":"Learning a Single Model With a Wide Range of Quality Factors for JPEG Image Artifacts Removal","display_name":"Learning a Single Model With a Wide Range of Quality Factors for JPEG Image Artifacts Removal","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3083244333","doi":"https://doi.org/10.1109/tip.2020.3020389","mag":"3083244333","pmid":"https://pubmed.ncbi.nlm.nih.gov/32886610"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3020389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3020389","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.06912","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jianwei Li","orcid":"https://orcid.org/0000-0003-4353-0960"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianwei Li","raw_affiliation_strings":["State Key Laboratory of Digital Publishing Technology, Peking University Founder Group Company Ltd., Beijing, China","Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Publishing Technology, Peking University Founder Group Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yongtao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongtao Wang","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haihua Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haihua Xie","raw_affiliation_strings":["State Key Laboratory of Digital Publishing Technology, Peking University Founder Group Company Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Digital Publishing Technology, Peking University Founder Group Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kai-Kuang Ma","orcid":"https://orcid.org/0000-0003-2932-5709"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Kai-Kuang Ma","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.6497,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.91777748,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"29","issue":null,"first_page":"8842","last_page":"8854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.6223999857902527,"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":"Advanced Image Processing Techniques","score":0.6223999857902527,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.17489999532699585,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.07840000092983246,"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/compression-artifact","display_name":"Compression artifact","score":0.832099974155426},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.6202999949455261},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.6136000156402588},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.6132000088691711},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5720000267028809},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5494999885559082},{"id":"https://openalex.org/keywords/jpeg-2000","display_name":"JPEG 2000","score":0.5077000260353088},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5049999952316284},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.5022000074386597},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.45730000734329224}],"concepts":[{"id":"https://openalex.org/C57654395","wikidata":"https://www.wikidata.org/wiki/Q1097775","display_name":"Compression artifact","level":5,"score":0.832099974155426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.772599995136261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7549999952316284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6735000014305115},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.6202999949455261},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.6136000156402588},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.6132000088691711},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5720000267028809},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5494999885559082},{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.5077000260353088},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.5022000074386597},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.45730000734329224},{"id":"https://openalex.org/C30684385","wikidata":"https://www.wikidata.org/wiki/Q176509","display_name":"Ringing","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4083999991416931},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.35670000314712524},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C180462255","wikidata":"https://www.wikidata.org/wiki/Q3559736","display_name":"Standard test image","level":4,"score":0.34610000252723694},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C17828673","wikidata":"https://www.wikidata.org/wiki/Q7334899","display_name":"Ringing artifacts","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C173642442","wikidata":"https://www.wikidata.org/wiki/Q1253346","display_name":"Decimation","level":3,"score":0.28760001063346863},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.258899986743927},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.2517000138759613}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2020.3020389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3020389","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:32886610","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32886610","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2009.06912","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.06912","pdf_url":"https://arxiv.org/pdf/2009.06912","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.06912","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.06912","pdf_url":"https://arxiv.org/pdf/2009.06912","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1946766895","https://openalex.org/W1965077907","https://openalex.org/W1978280057","https://openalex.org/W1984077106","https://openalex.org/W1985050490","https://openalex.org/W2071804214","https://openalex.org/W2099563019","https://openalex.org/W2110158442","https://openalex.org/W2110691818","https://openalex.org/W2117780481","https://openalex.org/W2133665775","https://openalex.org/W2140196014","https://openalex.org/W2141620200","https://openalex.org/W2142683286","https://openalex.org/W2146395539","https://openalex.org/W2161907179","https://openalex.org/W2170608748","https://openalex.org/W2194775991","https://openalex.org/W2203454327","https://openalex.org/W2294978292","https://openalex.org/W2423236762","https://openalex.org/W2466611277","https://openalex.org/W2476548250","https://openalex.org/W2508457857","https://openalex.org/W2519021537","https://openalex.org/W2553668389","https://openalex.org/W2557216475","https://openalex.org/W2562637781","https://openalex.org/W2741137940","https://openalex.org/W2764207251","https://openalex.org/W2784344583","https://openalex.org/W2897840030","https://openalex.org/W2962687329","https://openalex.org/W2962930383","https://openalex.org/W2963372104","https://openalex.org/W2964297221","https://openalex.org/W4241385190","https://openalex.org/W6631190155","https://openalex.org/W6636382570","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6684191040","https://openalex.org/W6704408313","https://openalex.org/W6758681311"],"related_works":["https://openalex.org/W23451984","https://openalex.org/W2114604705","https://openalex.org/W2080322084","https://openalex.org/W2377949240","https://openalex.org/W2972969201","https://openalex.org/W2361114818","https://openalex.org/W2611645609","https://openalex.org/W3003164983","https://openalex.org/W2085529769","https://openalex.org/W2131483079"],"abstract_inverted_index":{"Lossy":[0],"compression":[1,17,85,131,249],"brings":[2],"artifacts":[3,18,86,121,188,250],"into":[4],"the":[5,10,54,63,129,151,155,161,171,175,182,191,194,197,201,216,236,246,252],"compressed":[6],"image":[7,120,203,208],"and":[8,76,174,230,238],"degrades":[9],"visual":[11],"quality.":[12],"In":[13],"recent":[14],"years,":[15],"many":[16],"removal":[19,87,122,247],"methods":[20,35],"based":[21,40],"on":[22,41,128,213,228,245],"convolutional":[23],"neural":[24],"network":[25,88,96,148],"(CNN)":[26],"have":[27,233],"been":[28],"developed":[29],"with":[30,132,215],"great":[31],"success.":[32],"However,":[33],"these":[34],"usually":[36],"train":[37],"a":[38,46,82,98,108,142],"model":[39,100],"one":[42],"specific":[43],"value":[44,65],"or":[45,118],"small":[47],"range":[48,110],"of":[49,79,111,145,154,160,200,240,248],"quality":[50,57,112,133,209],"factors.":[51],"Obviously,":[52],"if":[53],"test":[55],"images":[56,232],"factor":[58],"does":[59],"not":[60],"match":[61],"to":[62,138],"assumed":[64],"range,":[66],"then":[67],"degraded":[68],"performance":[69],"will":[70],"be":[71,104],"resulted.":[72],"With":[73],"this":[74,92],"motivation":[75],"further":[77],"consideration":[78],"practical":[80],"usage,":[81],"highly":[83,206],"robust":[84],"is":[89,97],"proposed":[90,95,147,242],"in":[91,150,169],"paper.":[93],"Our":[94],"single":[99],"approach":[101,244],"that":[102,141,204],"can":[103],"trained":[105],"for":[106],"handling":[107],"wide":[109],"factors":[113],"while":[114],"consistently":[115],"delivering":[116],"superior":[117],"comparable":[119],"performance.":[123],"To":[124],"demonstrate,":[125],"we":[126],"focus":[127],"JPEG":[130],"factors,":[134],"ranging":[135],"from":[136,251],"1":[137],"60.":[139],"Note":[140],"turnkey":[143],"success":[144],"our":[146,241],"lies":[149],"novel":[152],"utilization":[153],"quantization":[156],"tables":[157],"as":[158,186,220],"part":[159],"training":[162],"data.":[163],"Furthermore,":[164],"it":[165],"has":[166],"two":[167],"branches":[168],"parallel-i.e.,":[170],"restoration":[172],"branch":[173],"global":[176,198,217],"branch.":[177],"The":[178],"former":[179],"effectively":[180],"removes":[181],"local":[183],"artifacts,":[184,218],"such":[185,219],"ringing":[187],"removal.":[189],"On":[190],"other":[192],"hand,":[193],"latter":[195],"extracts":[196],"features":[199],"entire":[202],"provides":[205],"instrumental":[207],"improvement,":[210],"especially":[211],"effective":[212],"dealing":[214],"blocking,":[221],"color":[222,229],"shifting.":[223],"Extensive":[224],"experimental":[225],"results":[226],"performed":[227],"grayscale":[231],"clearly":[234],"demonstrated":[235],"effectiveness":[237],"efficacy":[239],"single-model":[243],"decoded":[253],"image.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-09-11T00:00:00"}