{"id":"https://openalex.org/W2903122437","doi":"https://doi.org/10.1109/icpr.2018.8546087","title":"A New Single Image Super-resolution Method Using SIMK-based Classification and ISRM Technique","display_name":"A New Single Image Super-resolution Method Using SIMK-based Classification and ISRM Technique","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903122437","doi":"https://doi.org/10.1109/icpr.2018.8546087","mag":"2903122437"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546087","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5077113327","display_name":"Peiqi Duan","orcid":"https://orcid.org/0000-0002-4938-3132"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"funder","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiqi Duan","raw_affiliation_strings":["Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082092902","display_name":"Anlong Ming","orcid":"https://orcid.org/0000-0003-2952-7757"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"funder","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anlong Ming","raw_affiliation_strings":["Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083632340","display_name":"Xuejing Kang","orcid":"https://orcid.org/0000-0002-4088-351X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"funder","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuejing Kang","raw_affiliation_strings":["Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101678298","display_name":"Chao Yao","orcid":"https://orcid.org/0000-0001-5483-3225"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"funder","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Yao","raw_affiliation_strings":["Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.141,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":2,"citation_normalized_percentile":{"value":0.348101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":70,"max":74},"biblio":{"volume":null,"issue":null,"first_page":"3043","last_page":"3048"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998,"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":0.9998,"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":0.9968,"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":0.994,"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/overfitting","display_name":"Overfitting","score":0.5981389},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.43053707},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.42869},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42787898}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.66995996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6306449},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5981389},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.57960445},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4879567},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44856426},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.43895924},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.43053707},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42869},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42787898},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4277214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3714096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34617662},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28984162},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20426142},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18575743},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546087","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.77}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":17,"referenced_works":["https://openalex.org/W179179905","https://openalex.org/W2006262236","https://openalex.org/W2086479975","https://openalex.org/W2110158442","https://openalex.org/W2118963448","https://openalex.org/W2121058967","https://openalex.org/W2136396015","https://openalex.org/W2137290314","https://openalex.org/W2149669120","https://openalex.org/W2150081556","https://openalex.org/W2163836320","https://openalex.org/W2202656999","https://openalex.org/W2209642889","https://openalex.org/W2417716951","https://openalex.org/W2604671325","https://openalex.org/W54257720","https://openalex.org/W935139217"],"related_works":["https://openalex.org/W4378510483","https://openalex.org/W4362597605","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W3128011703","https://openalex.org/W2922073769","https://openalex.org/W2162970382","https://openalex.org/W2005234362","https://openalex.org/W1997235926","https://openalex.org/W1574414179"],"abstract_inverted_index":{"Single":[0],"image":[1],"super-resolution":[2],"(SR)":[3],"technique":[4],"is":[5,45,62,102,172],"widely":[6],"used":[7],"to":[8,59,110,156],"estimate":[9],"high-resolution":[10],"(HR)":[11],"images":[12],"from":[13,31,150,182],"low-resolution":[14],"(LR)":[15],"ones.":[16],"As":[17],"a":[18,71,177],"research":[19],"hotspot,":[20],"many":[21],"example-based":[22],"SR":[23,73,191],"methods":[24],"achieve":[25],"superior":[26],"results":[27],"by":[28,133],"learning":[29,88],"class-mapping-kernels":[30,61],"classified":[32],"external":[33],"LR-HR":[34,89],"patch-pair":[35],"samples.":[36],"However,":[37],"in":[38,87],"these":[39],"methods,":[40,189],"the":[41,49,55,92,98,103,126,141,144,151,158,169],"classification":[42,81,152],"of":[43,51,57,107,128,137,160],"samples":[44,112,130],"generally":[46],"based":[47,80,175],"on":[48,176],"features":[50],"LR":[52,138,167],"patch,":[53,168],"and":[54,82,113,124,196],"interference":[56],"ill-samples":[58,145],"learn":[60],"ignored":[63],"as":[64],"well.":[65],"In":[66,91,140,162],"this":[67],"paper,":[68],"we":[69,96],"propose":[70],"new":[72],"method":[74,192],"with":[75,187],"Sample":[76],"Individual":[77],"Mapping-Kernel":[78],"(SIMK)":[79],"Ill-Sample":[83],"Removal":[84],"Mechanism":[85],"(ISRM)":[86],"mapping.":[90],"proposed":[93],"sample":[94,117],"classification,":[95],"use":[97],"SIMK":[99],"feature":[100],"which":[101,146],"LR-to-HR":[104],"mapping":[105],"kernel":[106],"each":[108,165],"sample,":[109],"classify":[111],"obtain":[114],"more":[115],"reasonable":[116],"sets":[118],"for":[119,164],"mapping-learning.":[120],"To":[121],"prevent":[122],"overfitting":[123],"reduce":[125],"complexity":[127],"SIMK-based-classification,":[129],"are":[131,147,154],"pre-categorized":[132],"relative":[134],"pixel":[135],"values":[136],"patch.":[139],"mapping-learning":[142],"process,":[143],"far":[148],"away":[149],"center":[153],"removed":[155],"improve":[157],"validity":[159],"class-mapping-kernels.":[161],"addition,":[163],"testing":[166],"optimal":[170],"class":[171],"assigned":[173],"reasonably":[174],"probabilistic":[178],"decision":[179],"model":[180],"learned":[181],"Naive":[183],"Bayes":[184],"Classifier.":[185],"Comparing":[186],"state-of-the-art":[188],"our":[190],"achieves":[193],"both":[194],"visual":[195],"performance":[197],"improvement.":[198]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2903122437","counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-02-25T04:58:22.480268","created_date":"2018-12-11"}