{"id":"https://openalex.org/W3035232979","doi":"https://doi.org/10.1109/sam48682.2020.9104288","title":"Coupled Adversarial Learning for Single Image Super-Resolution","display_name":"Coupled Adversarial Learning for Single Image Super-Resolution","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3035232979","doi":"https://doi.org/10.1109/sam48682.2020.9104288","mag":"3035232979"},"language":"en","primary_location":{"id":"doi:10.1109/sam48682.2020.9104288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam48682.2020.9104288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5007305393","display_name":"Chih\u2013Chung Hsu","orcid":"https://orcid.org/0000-0002-2083-4438"},"institutions":[{"id":"https://openalex.org/I16566446","display_name":"National Pingtung University of Science and Technology","ror":"https://ror.org/01y6ccj36","country_code":"TW","type":"education","lineage":["https://openalex.org/I16566446"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Chung Hsu","raw_affiliation_strings":["Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan","institution_ids":["https://openalex.org/I16566446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035077218","display_name":"Kuan-Yu Huang","orcid":"https://orcid.org/0000-0002-1523-8009"},"institutions":[{"id":"https://openalex.org/I16566446","display_name":"National Pingtung University of Science and Technology","ror":"https://ror.org/01y6ccj36","country_code":"TW","type":"education","lineage":["https://openalex.org/I16566446"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuan-Yu Huang","raw_affiliation_strings":["Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan","institution_ids":["https://openalex.org/I16566446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16566446"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.06079743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9973000288009644,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9898999929428101,"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/feature","display_name":"Feature (linguistics)","score":0.7582424879074097},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7465773224830627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7333071231842041},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6286861896514893},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6283717751502991},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5717918276786804},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5194036960601807},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.517665684223175},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4920904040336609},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4353906214237213},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4165342450141907},{"id":"https://openalex.org/keywords/feature-detection","display_name":"Feature detection (computer vision)","score":0.41067758202552795},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39277756214141846},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.20839405059814453}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7582424879074097},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465773224830627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7333071231842041},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6286861896514893},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6283717751502991},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5717918276786804},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5194036960601807},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.517665684223175},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4920904040336609},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4353906214237213},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4165342450141907},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.41067758202552795},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39277756214141846},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.20839405059814453},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sam48682.2020.9104288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam48682.2020.9104288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W2108598243","https://openalex.org/W2121927366","https://openalex.org/W2194775991","https://openalex.org/W2304648132","https://openalex.org/W2476548250","https://openalex.org/W2549139847","https://openalex.org/W2607041014","https://openalex.org/W2741137940","https://openalex.org/W2765833400","https://openalex.org/W2866634454","https://openalex.org/W2891158090","https://openalex.org/W2944779197","https://openalex.org/W2963446712","https://openalex.org/W2963470893","https://openalex.org/W2964101377","https://openalex.org/W2964121744","https://openalex.org/W3011740795","https://openalex.org/W6727340567","https://openalex.org/W6754405603"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W4379791551","https://openalex.org/W4309346246","https://openalex.org/W1997438849","https://openalex.org/W2980144549","https://openalex.org/W3106568383","https://openalex.org/W1780126258","https://openalex.org/W4210656569","https://openalex.org/W4311555960","https://openalex.org/W4390871823"],"abstract_inverted_index":{"Generative":[0],"adversarial":[1,99],"nets":[2],"(GAN)":[3],"have":[4,62],"been":[5],"widely":[6],"used":[7,47],"in":[8,76],"several":[9],"image":[10,15,25,154],"restoration":[11],"tasks":[12],"such":[13],"as":[14,134,136],"denoise,":[16],"enhancement,":[17],"and":[18,38,56,91,125,149],"super-resolution.":[19],"The":[20],"objective":[21],"functions":[22],"of":[23,113],"an":[24],"super-resolution":[26,155],"problem":[27],"based":[28,102],"on":[29,103],"GANs":[30],"usually":[31,70],"are":[32],"reconstruction":[33],"error,":[34],"semantic":[35,43,126],"feature":[36,44,51,64,68,79,115,127],"distance,":[37],"GAN":[39,123],"loss.":[40],"In":[41,94,117],"general,":[42],"distance":[45],"was":[46],"to":[48,59,109,152],"measure":[49],"the":[50,54,67,73,78,86,111,114,118,131,138,144],"similarity":[52],"between":[53],"super-resolved":[55],"ground-truth":[57],"images,":[58],"ensure":[60],"they":[61],"similar":[63],"representations.":[65],"However,":[66],"is":[69,81,107,147],"extracted":[71,87],"by":[72],"pre-trained":[74],"model,":[75],"which":[77],"representation":[80],"not":[82],"designed":[83],"for":[84],"distinguishing":[85],"features":[88],"from":[89],"low-resolution":[90],"high-resolution":[92],"images.":[93],"this":[95],"study,":[96],"a":[97],"coupled":[98],"net":[100],"(CAN)":[101],"Siamese":[104],"Network":[105],"Structure":[106],"proposed,":[108],"improve":[110],"effectiveness":[112],"extraction.":[116],"proposed":[119,145],"CAN,":[120],"we":[121],"offer":[122],"loss":[124],"distances":[128],"simultaneously,":[129],"reducing":[130],"training":[132],"complexity":[133],"well":[135],"improving":[137],"performance.":[139],"Extensive":[140],"experiments":[141],"conducted":[142],"that":[143],"CAN":[146],"effective":[148],"efficient,":[150],"compared":[151],"state-of-the-art":[153],"schemes.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
