{"id":"https://openalex.org/W3142659587","doi":"https://doi.org/10.1109/tip.2021.3069317","title":"Deep Shearlet Residual Learning Network for Single Image Super-Resolution","display_name":"Deep Shearlet Residual Learning Network for Single Image Super-Resolution","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3142659587","doi":"https://doi.org/10.1109/tip.2021.3069317","mag":"3142659587","pmid":"https://pubmed.ncbi.nlm.nih.gov/33798084"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3069317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3069317","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","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/A5103055708","display_name":"Tianyu Geng","orcid":"https://orcid.org/0000-0003-2635-9807"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianyu Geng","raw_affiliation_strings":["Department of Electronic Information and Optical Engineering, Nankai University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Information and Optical Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405233","display_name":"Xiao-Yang Liu","orcid":"https://orcid.org/0000-0002-9532-1709"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao-Yang Liu","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100382658","display_name":"Xiaodong Wang","orcid":"https://orcid.org/0000-0002-2945-9240"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodong Wang","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101701886","display_name":"Guiling Sun","orcid":"https://orcid.org/0000-0001-5283-1760"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiling Sun","raw_affiliation_strings":["Department of Electronic Information and Optical Engineering, Nankai University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Information and Optical Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103055708"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":1.8253,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.87135621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"4129","last_page":"4142"},"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":0.9980999827384949,"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":0.9939000010490417,"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/shearlet","display_name":"Shearlet","score":0.8678172826766968},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.8397429585456848},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7650294303894043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6893541812896729},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6462895274162292},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5847568511962891},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5494394898414612},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4518064260482788},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4190284013748169},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3770734965801239},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19274821877479553}],"concepts":[{"id":"https://openalex.org/C67795661","wikidata":"https://www.wikidata.org/wiki/Q17018993","display_name":"Shearlet","level":3,"score":0.8678172826766968},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8397429585456848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7650294303894043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6893541812896729},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6462895274162292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5847568511962891},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5494394898414612},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4518064260482788},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4190284013748169},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3770734965801239},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19274821877479553},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2021.3069317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3069317","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:33798084","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33798084","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 image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3141261596","display_name":null,"funder_award_id":"61771262","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3268412544","display_name":null,"funder_award_id":"18ZXRHNC00140","funder_id":"https://openalex.org/F4320336756","funder_display_name":"Tianjin Science and Technology Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320336756","display_name":"Tianjin Science and Technology Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W1507554030","https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1791560514","https://openalex.org/W1836465849","https://openalex.org/W1885185971","https://openalex.org/W1974088225","https://openalex.org/W1976455710","https://openalex.org/W1981988107","https://openalex.org/W1983781364","https://openalex.org/W1997146646","https://openalex.org/W2020455334","https://openalex.org/W2028790650","https://openalex.org/W2029487796","https://openalex.org/W2036726483","https://openalex.org/W2046119925","https://openalex.org/W2054515210","https://openalex.org/W2062005868","https://openalex.org/W2067108728","https://openalex.org/W2069912449","https://openalex.org/W2100860054","https://openalex.org/W2103106600","https://openalex.org/W2105464873","https://openalex.org/W2114682356","https://openalex.org/W2117853853","https://openalex.org/W2121058967","https://openalex.org/W2121927366","https://openalex.org/W2122632184","https://openalex.org/W2137155471","https://openalex.org/W2141983208","https://openalex.org/W2155893237","https://openalex.org/W2157494358","https://openalex.org/W2194775991","https://openalex.org/W2242218935","https://openalex.org/W2359099468","https://openalex.org/W2508457857","https://openalex.org/W2509348655","https://openalex.org/W2542870090","https://openalex.org/W2592962403","https://openalex.org/W2621121458","https://openalex.org/W2776107444","https://openalex.org/W2789684973","https://openalex.org/W2810821452","https://openalex.org/W2866634454","https://openalex.org/W2884585870","https://openalex.org/W2895598217","https://openalex.org/W2902060108","https://openalex.org/W2904835235","https://openalex.org/W2956093359","https://openalex.org/W2956891660","https://openalex.org/W2963254280","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2964101377","https://openalex.org/W2964121744","https://openalex.org/W2966907728","https://openalex.org/W2974995978","https://openalex.org/W2986556279","https://openalex.org/W3101085605","https://openalex.org/W3103613945","https://openalex.org/W3103856189","https://openalex.org/W3104324122","https://openalex.org/W6631190155","https://openalex.org/W6638194035","https://openalex.org/W6638667902","https://openalex.org/W6753074096","https://openalex.org/W6753412334","https://openalex.org/W6755102754"],"related_works":["https://openalex.org/W2475220750","https://openalex.org/W2016759271","https://openalex.org/W2072476274","https://openalex.org/W2949924583","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2990636717","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Recently,":[0],"the":[1,9,20,26,57,62,71,81,87,92,122,132],"residual":[2,27,32,51,58],"learning":[3,52,135],"strategy":[4,98],"has":[5],"been":[6],"integrated":[7],"into":[8],"convolutional":[10],"neural":[11],"network":[12,53,67,140],"(CNN)":[13],"for":[14],"single":[15],"image":[16,33,111,118],"super-resolution":[17],"(SISR),":[18],"where":[19],"CNN":[21],"is":[22,68],"trained":[23,69],"to":[24,55,85,131],"estimate":[25,56],"images.":[28],"Recognizing":[29],"that":[30,121],"a":[31,48,95,100],"usually":[34],"consists":[35],"of":[36,80],"high-frequency":[37],"details":[38],"and":[39,99],"exhibits":[40],"cartoon-like":[41,82],"characteristics,":[42],"in":[43,70,129],"this":[44],"paper,":[45],"we":[46],"propose":[47],"deep":[49,134],"shearlet":[50,63,72,93],"(DSRLN)":[54],"images":[59],"based":[60],"on":[61,108],"transform.":[64],"The":[65],"proposed":[66,123],"transform-domain":[73],"which":[74],"provides":[75],"an":[76],"optimal":[77],"sparse":[78],"approximation":[79],"image.":[83],"Specifically,":[84],"address":[86],"large":[88],"statistical":[89],"variation":[90],"among":[91],"coefficients,":[94],"dual-path":[96],"training":[97],"data":[101],"weighting":[102],"technique":[103],"are":[104],"proposed.":[105],"Extensive":[106],"evaluations":[107],"general":[109],"natural":[110],"datasets":[112,119],"as":[113,115],"well":[114],"remote":[116],"sensing":[117],"show":[120],"DSRLN":[124],"scheme":[125],"achieves":[126],"close":[127],"results":[128],"PSNR":[130],"state-of-the-art":[133],"methods,":[136],"using":[137],"much":[138],"less":[139],"parameters.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}