{"id":"https://openalex.org/W2811206370","doi":"https://doi.org/10.1109/fskd.2017.8393189","title":"Boosted non-linear and non-negative sparse learning for single image super-resolution","display_name":"Boosted non-linear and non-negative sparse learning for single image super-resolution","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2811206370","doi":"https://doi.org/10.1109/fskd.2017.8393189","mag":"2811206370"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393189","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"},"type":"article","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/A5103086350","display_name":"Yungang Zhang","orcid":"https://orcid.org/0000-0002-8279-3109"},"institutions":[{"id":"https://openalex.org/I120825670","display_name":"Yunnan Normal University","ror":"https://ror.org/00sc9n023","country_code":"CN","type":"education","lineage":["https://openalex.org/I120825670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yungang Zhang","raw_affiliation_strings":["Department of Computer Science Yunnan Normal University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science Yunnan Normal University, Kunming, China","institution_ids":["https://openalex.org/I120825670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100513528","display_name":"Tianwei Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I120825670","display_name":"Yunnan Normal University","ror":"https://ror.org/00sc9n023","country_code":"CN","type":"education","lineage":["https://openalex.org/I120825670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianwei Xu","raw_affiliation_strings":["Graduate School Yunnan Normal University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"Graduate School Yunnan Normal University, Kunming, China","institution_ids":["https://openalex.org/I120825670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103086350"],"corresponding_institution_ids":["https://openalex.org/I120825670"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24502196,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"401","issue":null,"first_page":"2619","last_page":"2623"},"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":0.9991000294685364,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.995199978351593,"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/boosting","display_name":"Boosting (machine learning)","score":0.8148543834686279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7252793312072754},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.659818172454834},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6516616344451904},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6137225031852722},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5832633972167969},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5282180309295654},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.504543662071228},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48919203877449036},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42503881454467773},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3222001791000366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2553645372390747}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8148543834686279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7252793312072754},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.659818172454834},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6516616344451904},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6137225031852722},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5832633972167969},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5282180309295654},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.504543662071228},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48919203877449036},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42503881454467773},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3222001791000366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2553645372390747},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393189","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7200000286102295,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W7682646","https://openalex.org/W1552766422","https://openalex.org/W1902027874","https://openalex.org/W2000364018","https://openalex.org/W2004465977","https://openalex.org/W2030321677","https://openalex.org/W2065050375","https://openalex.org/W2082025208","https://openalex.org/W2088254198","https://openalex.org/W2107916269","https://openalex.org/W2112447569","https://openalex.org/W2116216716","https://openalex.org/W2118963448","https://openalex.org/W2124438045","https://openalex.org/W2124868070","https://openalex.org/W2131689618","https://openalex.org/W2153635508","https://openalex.org/W2160547390","https://openalex.org/W2161516371","https://openalex.org/W2163112044","https://openalex.org/W2332771693","https://openalex.org/W2487087946","https://openalex.org/W2513514719","https://openalex.org/W2534320940","https://openalex.org/W6600294690","https://openalex.org/W6666411865","https://openalex.org/W6676727762","https://openalex.org/W6683660953"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W2905156999","https://openalex.org/W4229460275","https://openalex.org/W4296079469","https://openalex.org/W1987518466","https://openalex.org/W3135046080","https://openalex.org/W3023033471","https://openalex.org/W2014852328","https://openalex.org/W2809161969"],"abstract_inverted_index":{"In":[0,68],"image":[1,16,34,105],"super-resolution":[2,17],"area,":[3],"the":[4,30,48,63,73,80,84,108,116,132,136,139],"effectiveness":[5],"of":[6,32,71,83,104,138],"sparse":[7,21,66,124],"learning":[8,22,42],"methods":[9],"have":[10,97],"been":[11],"demonstrated,":[12],"especially":[13],"in":[14,26,52],"single":[15,33],"applications.":[18],"A":[19],"novel":[20],"method":[23],"is":[24,44,59,112],"proposed":[25,37,121,140],"this":[27],"paper":[28],"for":[29,114],"task":[31],"super-resolution.":[35],"The":[36,90,120],"non-linear":[38],"and":[39],"nonnegative":[40],"sparsity-based":[41],"model":[43,125],"used":[45,60],"to":[46,61,79],"capture":[47],"nonlinear":[49],"data":[50],"distributions":[51],"images.":[53],"Moreover,":[54],"a":[55],"boosting":[56],"ensemble":[57,93],"strategy":[58],"improve":[62],"learned":[64,85],"`weak'":[65],"model.":[67],"each":[69],"round":[70],"boosting,":[72],"training":[74],"images":[75],"are":[76],"chosen":[77],"according":[78],"reconstruction":[81,99],"errors":[82],"models":[86],"from":[87],"previous":[88],"rounds.":[89],"obtained":[91],"dictionary":[92],"contains":[94],"dictionaries":[95],"which":[96],"different":[98,102],"capability":[100],"on":[101,128],"types":[103],"patches.":[106],"Then":[107],"support":[109],"vector":[110],"regression":[111],"applied":[113],"obtaining":[115],"final":[117],"result":[118],"image.":[119],"non-negative":[122],"kernel":[123],"was":[126],"tested":[127],"popular":[129],"benchmark":[130],"images,":[131],"experimental":[133],"results":[134],"illustrate":[135],"competitiveness":[137],"method.":[141]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
