{"id":"https://openalex.org/W4392297328","doi":"https://doi.org/10.1007/s00371-024-03276-8","title":"High-frequency channel attention and contrastive learning for image super-resolution","display_name":"High-frequency channel attention and contrastive learning for image super-resolution","publication_year":2024,"publication_date":"2024-02-29","ids":{"openalex":"https://openalex.org/W4392297328","doi":"https://doi.org/10.1007/s00371-024-03276-8"},"language":"en","primary_location":{"id":"doi:10.1007/s00371-024-03276-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00371-024-03276-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00371-024-03276-8.pdf","source":{"id":"https://openalex.org/S73060445","display_name":"The Visual Computer","issn_l":"0178-2789","issn":["0178-2789","1432-2315"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Visual Computer","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00371-024-03276-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009706910","display_name":"Tianyu Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Tianyu Yan","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055149475","display_name":"Hujun Yin","orcid":"https://orcid.org/0000-0002-9198-5401"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hujun Yin","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009706910"],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.0146,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.751961,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"40","issue":"12","first_page":"8839","last_page":"8851"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":1.0,"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":1.0,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9977999925613403,"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/computer-graphics","display_name":"Computer graphics","score":0.6732078194618225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6178497672080994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48808133602142334},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47631773352622986},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.46927276253700256},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.34685903787612915},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3420320153236389},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11032557487487793}],"concepts":[{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.6732078194618225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6178497672080994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48808133602142334},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47631773352622986},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.46927276253700256},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.34685903787612915},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3420320153236389},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11032557487487793}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00371-024-03276-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00371-024-03276-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00371-024-03276-8.pdf","source":{"id":"https://openalex.org/S73060445","display_name":"The Visual Computer","issn_l":"0178-2789","issn":["0178-2789","1432-2315"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Visual Computer","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/c6f8448d-95e8-4ff6-a7f9-aa04845be6c2","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/c6f8448d-95e8-4ff6-a7f9-aa04845be6c2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Yan, T & Yin, H 2024, 'High-Frequency Channel Attention and Contrastive Learning for Image Super-Resolution', Visual Computer. https://doi.org/10.1007/s00371-024-03276-8","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s00371-024-03276-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00371-024-03276-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00371-024-03276-8.pdf","source":{"id":"https://openalex.org/S73060445","display_name":"The Visual Computer","issn_l":"0178-2789","issn":["0178-2789","1432-2315"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Visual Computer","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392297328.pdf"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W2044451987","https://openalex.org/W2047920195","https://openalex.org/W2112024783","https://openalex.org/W2121927366","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2331128040","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2503458650","https://openalex.org/W2607041014","https://openalex.org/W2740139074","https://openalex.org/W2741137940","https://openalex.org/W2747898905","https://openalex.org/W2752782242","https://openalex.org/W2780544323","https://openalex.org/W2866634454","https://openalex.org/W2884585870","https://openalex.org/W2954930822","https://openalex.org/W2963372104","https://openalex.org/W2963446712","https://openalex.org/W2963645458","https://openalex.org/W2963986095","https://openalex.org/W2964101377","https://openalex.org/W2964125708","https://openalex.org/W2970971581","https://openalex.org/W2976718572","https://openalex.org/W3034247386","https://openalex.org/W3041327056","https://openalex.org/W3043875305","https://openalex.org/W3083579885","https://openalex.org/W3088103684","https://openalex.org/W3123866747","https://openalex.org/W3158187990","https://openalex.org/W3176997885","https://openalex.org/W3190446228","https://openalex.org/W3202277597","https://openalex.org/W4214666412","https://openalex.org/W4221147148","https://openalex.org/W4221154746","https://openalex.org/W4224307186","https://openalex.org/W4226284371","https://openalex.org/W4280491778","https://openalex.org/W4283384135","https://openalex.org/W4283785001","https://openalex.org/W4288035257","https://openalex.org/W4292787304","https://openalex.org/W4321242367","https://openalex.org/W4376115880","https://openalex.org/W6601865935","https://openalex.org/W6945244495"],"related_works":["https://openalex.org/W2319989118","https://openalex.org/W1503820821","https://openalex.org/W2285491073","https://openalex.org/W1991998366","https://openalex.org/W3112751614","https://openalex.org/W1995979513","https://openalex.org/W642358674","https://openalex.org/W4249185555","https://openalex.org/W1999937205","https://openalex.org/W2517707608"],"abstract_inverted_index":{"Abstract":[0],"Over":[1],"the":[2,54,88,91,94,105,113,116,138,152,175],"last":[3],"decade,":[4],"convolutional":[5],"neural":[6],"networks":[7,177],"(CNNs)":[8],"have":[9],"allowed":[10],"remarkable":[11],"advances":[12],"in":[13,62],"single":[14],"image":[15],"super-resolution":[16],"(SISR).":[17],"In":[18,47],"general,":[19],"recovering":[20],"high-frequency":[21,60,74,110,130,155],"features":[22,29,36,61],"is":[23],"crucial":[24],"for":[25],"high-performance":[26],"models.":[27],"High-frequency":[28],"suffer":[30],"more":[31,58],"serious":[32],"damages":[33],"than":[34],"low-frequency":[35],"during":[37],"downscaling,":[38],"making":[39],"it":[40,165],"hard":[41],"to":[42,52,56,86,122,150,174],"recover":[43],"edges":[44],"and":[45,67,79,108,142],"textures.":[46],"this":[48],"paper,":[49],"we":[50,71],"attempt":[51],"guide":[53],"network":[55,158],"focus":[57],"on":[59],"restoration":[63],"from":[64],"both":[65],"channel":[66,75,156],"spatial":[68,114,125],"perspectives.":[69],"Specifically,":[70],"propose":[72],"a":[73,80,124],"attention":[76],"(HFCA)":[77],"module":[78,96,141],"frequency":[81],"contrastive":[82,120,157],"learning":[83,121],"(FCL)":[84],"loss":[85,118,144],"aid":[87],"process.":[89],"For":[90,112],"channel-wise":[92],"perspective,":[93,115],"HFCA":[95,140],"rescales":[97],"channels":[98],"by":[99],"predicting":[100],"statistical":[101],"similarity":[102],"metrics":[103],"of":[104,178],"feature":[106],"maps":[107],"their":[109],"components.":[111],"FCL":[117,143],"introduces":[119],"train":[123],"mask":[126],"that":[127,164],"adaptively":[128],"assigns":[129],"areas":[131],"with":[132],"large":[133],"scaling":[134],"factors.":[135],"We":[136],"incorporate":[137],"proposed":[139,153],"into":[145],"an":[146],"EDSR":[147],"baseline":[148],"model":[149,180],"construct":[151],"lightweight":[154],"(HFCCN).":[159],"Extensive":[160],"experimental":[161],"results":[162],"show":[163],"can":[166],"yield":[167],"markedly":[168],"improved":[169],"or":[170],"competitive":[171],"performances":[172],"compared":[173],"state-of-the-art":[176],"similar":[179],"parameters.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
