{"id":"https://openalex.org/W4411551755","doi":"https://doi.org/10.1109/tnnls.2025.3577669","title":"TC3Net: Transformer and Convolution Coupled Contrastive Network for Single Image Super-Resolution","display_name":"TC3Net: Transformer and Convolution Coupled Contrastive Network for Single Image Super-Resolution","publication_year":2025,"publication_date":"2025-06-23","ids":{"openalex":"https://openalex.org/W4411551755","doi":"https://doi.org/10.1109/tnnls.2025.3577669","pmid":"https://pubmed.ncbi.nlm.nih.gov/40549521"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3577669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3577669","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","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":"https://openalex.org/A5101436881","display_name":"Licheng Liu","orcid":"https://orcid.org/0000-0003-4891-9211"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licheng Liu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-4891-9211","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qibin Zhang","orcid":"https://orcid.org/0009-0005-7671-7723"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qibin Zhang","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0005-7671-7723","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028255247","display_name":"Tingyun Liu","orcid":"https://orcid.org/0000-0001-6898-4489"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingyun Liu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-6898-4489","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100643265","display_name":"C. L. Philip Chen","orcid":"https://orcid.org/0000-0001-5451-7230"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"C. L. Philip Chen","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5451-7230","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7655,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71311793,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"36","issue":"10","first_page":"17953","last_page":"17965"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9925000071525574,"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":0.9925000071525574,"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":0.9908000230789185,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.982699990272522,"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/transformer","display_name":"Transformer","score":0.552743673324585},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4888753294944763},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44776564836502075},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4379293918609619},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.415071576833725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36861610412597656},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13527169823646545},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10829389095306396},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10003894567489624},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.05383971333503723}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.552743673324585},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4888753294944763},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44776564836502075},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4379293918609619},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.415071576833725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36861610412597656},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13527169823646545},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10829389095306396},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10003894567489624},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.05383971333503723}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3577669","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3577669","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40549521","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40549521","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 neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5471373392","display_name":null,"funder_award_id":"2025A1515010293","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G8173004958","display_name":null,"funder_award_id":"2025A1515010293","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G8904910473","display_name":"\u9762\u5411\u65e0\u7ea6\u675f\u76d1\u63a7\u573a\u666f\u7684\u566a\u58f0\u4eba\u8138\u56fe\u50cf\u9c81\u68d2\u8d85\u5206\u8fa8\u7387\u6280\u672f\u7814\u7a76","funder_award_id":"62071174","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W2047920195","https://openalex.org/W2121927366","https://openalex.org/W2133665775","https://openalex.org/W2192954843","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2528169004","https://openalex.org/W2741137940","https://openalex.org/W2866634454","https://openalex.org/W2927933146","https://openalex.org/W2963372104","https://openalex.org/W2963420686","https://openalex.org/W2964101377","https://openalex.org/W2969200668","https://openalex.org/W3013529009","https://openalex.org/W3032400974","https://openalex.org/W3083579885","https://openalex.org/W3092755853","https://openalex.org/W3137952714","https://openalex.org/W3138516171","https://openalex.org/W3171125843","https://openalex.org/W3173269149","https://openalex.org/W3174531399","https://openalex.org/W3175593095","https://openalex.org/W3177052299","https://openalex.org/W3184054654","https://openalex.org/W3189480530","https://openalex.org/W3207918547","https://openalex.org/W4223616928","https://openalex.org/W4225672218","https://openalex.org/W4285034124","https://openalex.org/W4287020683","https://openalex.org/W4306167924","https://openalex.org/W4310720045","https://openalex.org/W4312283145","https://openalex.org/W4313152909","https://openalex.org/W4319300717","https://openalex.org/W4319300887","https://openalex.org/W4320008741","https://openalex.org/W4323519308","https://openalex.org/W4360887305","https://openalex.org/W4383753076","https://openalex.org/W4385245566","https://openalex.org/W4386083034","https://openalex.org/W4386453635","https://openalex.org/W4387986992","https://openalex.org/W4388519991","https://openalex.org/W4390706297","https://openalex.org/W4390872611","https://openalex.org/W4392693690","https://openalex.org/W4392824986","https://openalex.org/W4402904179","https://openalex.org/W4405908078"],"related_works":["https://openalex.org/W1517180214","https://openalex.org/W2082780921","https://openalex.org/W4387838477","https://openalex.org/W2025517136","https://openalex.org/W2028664052","https://openalex.org/W2067193074","https://openalex.org/W2182785089","https://openalex.org/W634414395","https://openalex.org/W4312178642","https://openalex.org/W2056165575"],"abstract_inverted_index":{"The":[0,103],"convolutional":[1,43],"neural":[2],"network":[3,85],"(CNN)":[4],"and":[5,45,81,101,122,142,155,170,182,208],"transformer":[6,80,117,169],"have":[7],"gained":[8],"significant":[9],"attention":[10,139],"in":[11,24,50,63,70,197],"the":[12,40,53,60,64,96,132,137,143,168,184,198],"field":[13],"of":[14,32,98,109,136,200],"single":[15],"image":[16,46,161],"super-resolution":[17],"(SISR),":[18],"owing":[19],"to":[20,94,151,177],"their":[21,35,179],"powerful":[22],"capacity":[23],"nonlinear":[25],"feature":[26,113,118,129,145,153,172],"extraction.":[27,130],"Nonetheless,":[28],"these":[29,68],"two":[30],"types":[31],"approaches":[33],"hold":[34],"own":[36],"limitations.":[37],"For":[38],"instance,":[39],"interaction":[41],"between":[42,167,205],"kernels":[44],"content":[47],"is":[48,106,149,174],"agnostic":[49],"CNN,":[51],"while":[52],"computational":[54],"complexity":[55],"increases":[56],"quadratically":[57],"along":[58],"with":[59],"spatial":[61],"resolution":[62],"transformer.":[65,102],"To":[66],"address":[67],"concerns,":[69],"this":[71],"article,":[72],"we":[73],"propose":[74],"a":[75,91,164,202],"novel":[76],"unified":[77],"framework":[78],"named":[79],"convolution":[82],"coupled":[83,123,138,157],"contrastive":[84,124,165],"(TC3Net)":[86],"for":[87,127,159],"SISR,":[88],"which":[89],"holds":[90],"triple-branch":[92],"structure":[93],"integrate":[95],"merits":[97],"both":[99],"CNN":[100,112,171],"proposed":[104],"TC3Net":[105,191],"mainly":[107],"composed":[108],"several":[110,193],"stacked":[111],"extraction":[114,119,146],"(CFE)":[115],"blocks,":[116,121],"(TFE)":[120],"blocks":[125],"(CCBs)":[126],"diverse":[128],"Particularly,":[131],"CCB":[133],"that":[134,190],"consists":[135],"block":[140,148],"(CAB)":[141],"local-global":[144],"(LGFE)":[147],"designed":[150],"fuse":[152],"maps":[154,173],"extract":[156],"information":[158],"better":[160,203],"reconstruction.":[162],"Moreover,":[163],"loss":[166],"further":[175],"introduced":[176],"enhance":[178],"discriminative":[180],"characteristics":[181],"complement":[183],"fused":[185],"features.":[186],"Experimental":[187],"results":[188],"demonstrate":[189],"outperforms":[192],"state-of-the-art":[194],"(SOTA)":[195],"methods":[196],"aspect":[199],"achieving":[201],"balance":[204],"model":[206],"size":[207],"performance.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
