{"id":"https://openalex.org/W3101187320","doi":"https://doi.org/10.1109/tip.2021.3074821","title":"Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution","display_name":"Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3101187320","doi":"https://doi.org/10.1109/tip.2021.3074821","mag":"3101187320","pmid":"https://pubmed.ncbi.nlm.nih.gov/33905331"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3074821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3074821","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":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2011.07068","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Santiago Lopez-Tapia","orcid":"https://orcid.org/0000-0003-2090-7446"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Santiago Lopez-Tapia","raw_affiliation_strings":["Universidad de Granada, Granada, Spain"],"raw_orcid":"https://orcid.org/0000-0003-2090-7446","affiliations":[{"raw_affiliation_string":"Universidad de Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"last","author":{"id":null,"display_name":"Nicolas Perez de la Blanca","orcid":null},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Nicolas Perez de la Blanca","raw_affiliation_strings":["Universidad de Granada, Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I173304897"],"apc_list":null,"apc_paid":null,"fwci":0.8171,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.69788713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"30","issue":null,"first_page":"4747","last_page":"4759"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9376000165939331,"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":0.9376000165939331,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.031700000166893005,"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/T11897","display_name":"Digital Holography and Microscopy","score":0.0038999998942017555,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deblurring","display_name":"Deblurring","score":0.7689999938011169},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7293000221252441},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6365000009536743},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.47749999165534973},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4650000035762787},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4643000066280365},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.45669999718666077},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4413999915122986},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.43880000710487366},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.41359999775886536}],"concepts":[{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.7689999938011169},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7293000221252441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7081999778747559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6549999713897705},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6365000009536743},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.47749999165534973},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4650000035762787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.45669999718666077},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4271000027656555},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.41359999775886536},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.35499998927116394},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3319999873638153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.3215000033378601},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.31790000200271606},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3174999952316284},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2985000014305115},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C2777851325","wikidata":"https://www.wikidata.org/wiki/Q7094102","display_name":"Online model","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C108598597","wikidata":"https://www.wikidata.org/wiki/Q124255","display_name":"Conic section","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25369998812675476}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2021.3074821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3074821","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:33905331","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33905331","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},{"id":"pmh:oai:arXiv.org:2011.07068","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.07068","pdf_url":"https://arxiv.org/pdf/2011.07068","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2011.07068","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.07068","pdf_url":"https://arxiv.org/pdf/2011.07068","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G112021503","display_name":null,"funder_award_id":"PID2019-105142RB-C22 / AEI / 10.13039/501100011033","funder_id":"https://openalex.org/F4320335598","funder_display_name":"Agencia Estatal de Investigaci\u00f3n"},{"id":"https://openalex.org/G1276317268","display_name":null,"funder_award_id":"H2020-MSCA ITN-2019","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320335598","display_name":"Agencia Estatal de Investigaci\u00f3n","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W7682646","https://openalex.org/W54257720","https://openalex.org/W1930824406","https://openalex.org/W1996726072","https://openalex.org/W2023902556","https://openalex.org/W2044945560","https://openalex.org/W2049237558","https://openalex.org/W2067108728","https://openalex.org/W2088254198","https://openalex.org/W2103346247","https://openalex.org/W2121927366","https://openalex.org/W2123613719","https://openalex.org/W2138598313","https://openalex.org/W2173379916","https://openalex.org/W2189021291","https://openalex.org/W2192954843","https://openalex.org/W2200125188","https://openalex.org/W2242218935","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2507550467","https://openalex.org/W2508457857","https://openalex.org/W2537458122","https://openalex.org/W2573726823","https://openalex.org/W2589026418","https://openalex.org/W2601564443","https://openalex.org/W2613155248","https://openalex.org/W2620724120","https://openalex.org/W2739757502","https://openalex.org/W2741137940","https://openalex.org/W2798664922","https://openalex.org/W2866634454","https://openalex.org/W2883804791","https://openalex.org/W2949128855","https://openalex.org/W2962814024","https://openalex.org/W2962903125","https://openalex.org/W2963182372","https://openalex.org/W2963312584","https://openalex.org/W2963372104","https://openalex.org/W2963704386","https://openalex.org/W2963774720","https://openalex.org/W2963814095","https://openalex.org/W2964013315","https://openalex.org/W2964101377","https://openalex.org/W2964277374","https://openalex.org/W3040726448","https://openalex.org/W4211167357","https://openalex.org/W4292363360","https://openalex.org/W6696085341","https://openalex.org/W6716109767","https://openalex.org/W6727340567","https://openalex.org/W6744107740","https://openalex.org/W6753074096","https://openalex.org/W6754405603","https://openalex.org/W6756800942","https://openalex.org/W6766978945","https://openalex.org/W6783772115"],"related_works":[],"abstract_inverted_index":{"Single":[0],"Image":[1],"Super-Resolution":[2],"(SISR)":[3],"is":[4,98,113,121,144,193,240],"one":[5,196],"of":[6,31,86,118,140,159,203,217,221,235,246],"the":[7,18,29,40,78,87,116,151,160,165,181,184,194,229,238,247],"low-level":[8],"computer":[9],"vision":[10],"problems":[11],"that":[12,69,190],"has":[13],"received":[14],"increased":[15],"attention":[16],"in":[17,71,147,177,233],"last":[19],"few":[20],"years.":[21],"Current":[22],"approaches":[23],"are":[24],"primarily":[25],"based":[26],"on":[27,130,228],"harnessing":[28],"power":[30],"deep":[32],"learning":[33],"models":[34],"and":[35,81,179,242],"optimization":[36],"techniques":[37],"to":[38,44,100,127,142,198,253,263],"reverse":[39],"degradation":[41,79],"model.":[42],"Owing":[43],"its":[45,131],"hardness,":[46],"isotropic":[47],"blurring":[48],"or":[49,106],"Gaussians":[50],"with":[51,172,183],"small":[52],"anisotropic":[53],"deformations":[54],"have":[55],"been":[56],"mainly":[57],"considered.":[58],"Here,":[59],"we":[60,136],"widen":[61],"this":[62,138],"scenario":[63],"by":[64,164],"including":[65],"large":[66],"non-Gaussian":[67],"blurs":[68],"arise":[70],"real":[72],"camera":[73],"movements.":[74],"Our":[75],"approach":[76,239],"leverages":[77],"model":[80,171,192,207,225],"proposes":[82],"a":[83,102,145,154,214,260],"new":[84,109],"formulation":[85],"Convolutional":[88],"Neural":[89],"Network":[90],"(CNN)":[91],"cascade":[92],"model,":[93,153],"where":[94,115],"each":[95,119],"network":[96],"sub-module":[97,120,156],"constrained":[99],"solve":[101],"specific":[103,132],"degradation:":[104],"deblurring":[105],"upsampling.":[107],"A":[108],"densely":[110],"connected":[111],"CNN-architecture":[112],"proposed":[114],"output":[117],"restricted":[122],"using":[123],"some":[124],"external":[125],"knowledge":[126],"focus":[128],"it":[129,250,259],"task.":[133],"As":[134],"far":[135],"know,":[137],"use":[139],"domain-knowledge":[141],"module-level":[143],"novelty":[146],"SISR.":[148],"To":[149],"fit":[150],"finest":[152],"final":[155],"takes":[157],"care":[158],"residual":[161],"errors":[162],"propagated":[163],"previous":[166],"sub-modules.":[167],"We":[168],"check":[169],"our":[170,191,200,206,224],"three":[173],"state-of-the-art":[174],"(SOTA)":[175],"datasets":[176],"SISR":[178],"compare":[180],"results":[182,188],"SOTA":[185,211],"models.":[186,265],"The":[187],"show":[189],"only":[195],"able":[197],"manage":[199],"wider":[201],"set":[202,216],"deformations.":[204,218],"Furthermore,":[205],"overcomes":[208],"all":[209],"current":[210],"methods":[212],"for":[213],"standard":[215],"In":[219],"terms":[220,234],"computational":[222],"load,":[223],"also":[226],"improves":[227],"two":[230],"closest":[231],"competitors":[232],"efficiency.":[236],"Although":[237],"non-blind":[241],"requires":[243],"an":[244],"estimation":[245,256],"blur":[248,254],"kernel,":[249],"shows":[251],"robustness":[252],"kernel":[255],"errors,":[257],"making":[258],"good":[261],"alternative":[262],"blind":[264]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2020-11-23T00:00:00"}
