{"id":"https://openalex.org/W4409356431","doi":"https://doi.org/10.1109/tip.2025.3558442","title":"Content-Decoupled Contrastive Learning-Based Implicit Degradation Modeling for Blind Image Super-Resolution","display_name":"Content-Decoupled Contrastive Learning-Based Implicit Degradation Modeling for Blind Image Super-Resolution","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409356431","doi":"https://doi.org/10.1109/tip.2025.3558442","pmid":"https://pubmed.ncbi.nlm.nih.gov/40215148"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2025.3558442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2025.3558442","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":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jiang Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Yuan","raw_affiliation_strings":["State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047659529","display_name":"Ji Ma","orcid":"https://orcid.org/0000-0002-1437-464X"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Ma","raw_affiliation_strings":["State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100408109","display_name":"Bo Wang","orcid":"https://orcid.org/0000-0002-5596-1795"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Wang","raw_affiliation_strings":["State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5596-1795","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114549594","display_name":"Weiming Hu","orcid":"https://orcid.org/0000-0001-9237-8825"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiming Hu","raw_affiliation_strings":["State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9237-8825","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA, Beijing, China","institution_ids":["https://openalex.org/I4210112150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":1.5311,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81596123,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"34","issue":null,"first_page":"4751","last_page":"4766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9973999857902527,"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":0.9973999857902527,"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":0.9926999807357788,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.6069697141647339},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6064643263816833},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.5859227776527405},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5379500985145569},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.4816931188106537},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4741598665714264},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4303149878978729},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42385923862457275},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.41329053044319153},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39490240812301636},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3684576451778412}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6069697141647339},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6064643263816833},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.5859227776527405},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5379500985145569},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.4816931188106537},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4741598665714264},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4303149878978729},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42385923862457275},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.41329053044319153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39490240812301636},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3684576451778412},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2025.3558442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2025.3558442","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:40215148","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40215148","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}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1520754625","display_name":null,"funder_award_id":"4234086","funder_id":"https://openalex.org/F4320334977","funder_display_name":"Beijing Municipal Natural Science Foundation"},{"id":"https://openalex.org/G2688969607","display_name":null,"funder_award_id":"62192782","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334977","display_name":"Beijing Municipal Natural Science Foundation","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1791560514","https://openalex.org/W1821462560","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W2047920195","https://openalex.org/W2121927366","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2242218935","https://openalex.org/W2476548250","https://openalex.org/W2508457857","https://openalex.org/W2739757502","https://openalex.org/W2741137940","https://openalex.org/W2752782242","https://openalex.org/W2842511635","https://openalex.org/W2866634454","https://openalex.org/W2962785568","https://openalex.org/W2962814024","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2964277374","https://openalex.org/W2983339877","https://openalex.org/W3016355810","https://openalex.org/W3035524453","https://openalex.org/W3168684807","https://openalex.org/W3207918547","https://openalex.org/W4206075922","https://openalex.org/W4220911136","https://openalex.org/W4280533919","https://openalex.org/W4280541456","https://openalex.org/W4312443924","https://openalex.org/W4312458589","https://openalex.org/W4312509301","https://openalex.org/W4312780519","https://openalex.org/W4380303540","https://openalex.org/W4385245566","https://openalex.org/W4390604941","https://openalex.org/W4390872911","https://openalex.org/W4392061700","https://openalex.org/W4392251820","https://openalex.org/W4392945004","https://openalex.org/W4394698785","https://openalex.org/W4402817068","https://openalex.org/W6726497184","https://openalex.org/W6733590821","https://openalex.org/W6736057607","https://openalex.org/W6755977528","https://openalex.org/W6757817989","https://openalex.org/W6784110152","https://openalex.org/W6850881337"],"related_works":["https://openalex.org/W2534928293","https://openalex.org/W2150099345","https://openalex.org/W2160318243","https://openalex.org/W2357322570","https://openalex.org/W1490077415","https://openalex.org/W2997591215","https://openalex.org/W2227541280","https://openalex.org/W3004580327","https://openalex.org/W4386158998","https://openalex.org/W2537408670"],"abstract_inverted_index":{"Implicit":[0],"degradation":[1,21,32,84,103,119,128,135,178],"modeling-based":[2],"blind":[3,58,66,195],"super-resolution":[4,60],"(SR)":[5],"has":[6],"attracted":[7],"more":[8,30],"increasing":[9],"attention":[10],"in":[11,86,176],"the":[12,43,64,77,82,106,111,116,143,153,188],"community":[13],"due":[14],"to":[15,19,28,38,45,80,96,137],"its":[16],"excellent":[17],"generalization":[18],"complex":[20],"scenarios":[22],"and":[23,34,102,113,162,173,184,193,199],"wide":[24],"application":[25],"range.":[26],"How":[27],"extract":[29],"discriminative":[31],"representations":[33,136],"fully":[35],"adapt":[36,134],"them":[37],"specific":[39,138],"image":[40,59,149],"features":[41,101,104,140],"is":[42,94],"key":[44],"this":[46,49],"task.":[47],"In":[48,121],"paper,":[50],"we":[51,123],"propose":[52,124],"a":[53,88,125],"new":[54,89],"Content-decoupled":[55],"Contrastive":[56],"Learning-based":[57],"(CdCL)":[61],"framework":[62,70],"following":[63],"typical":[65],"SR":[67,155,196],"pipeline.":[68],"This":[69],"introduces":[71],"negative-free":[72],"contrastive":[73],"learning":[74],"technique":[75],"for":[76],"first":[78],"time":[79],"model":[81,156],"implicit":[83,118,127],"representation,":[85],"which":[87],"cyclic":[90],"shift":[91],"sampling":[92],"strategy":[93],"designed":[95],"ensure":[97],"decoupling":[98],"between":[99],"content":[100],"from":[105],"data":[107,164],"perspective,":[108],"thereby":[109],"improving":[110],"purity":[112],"discriminability":[114],"of":[115,148,190],"learned":[117],"space.":[120],"addition,":[122],"detail-aware":[126],"adapting":[129],"module":[130],"that":[131,166],"can":[132],"better":[133],"LR":[139],"by":[141],"enhancing":[142],"basic":[144],"adaptation":[145],"unit's":[146],"perception":[147],"details,":[150],"significantly":[151],"reducing":[152,182],"overall":[154],"complexity.":[157],"Extensive":[158],"experiments":[159],"on":[160],"synthetic":[161],"real":[163],"show":[165],"our":[167],"method":[168],"achieves":[169],"highly":[170],"competitive":[171],"quantitative":[172],"qualitative":[174],"results":[175],"various":[177],"settings":[179],"while":[180],"obviously":[181],"parameters":[183],"computational":[185],"costs,":[186],"validating":[187],"feasibility":[189],"designing":[191],"practical":[192],"lightweight":[194],"tools.":[197],"Codes":[198],"models":[200],"will":[201],"be":[202],"available":[203],"at":[204],"https://github.com/Fieldhunter/CdCL.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-04-12T00:00:00"}