{"id":"https://openalex.org/W2971980782","doi":"https://doi.org/10.1145/3349801.3349823","title":"Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations","display_name":"Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations","publication_year":2019,"publication_date":"2019-09-09","ids":{"openalex":"https://openalex.org/W2971980782","doi":"https://doi.org/10.1145/3349801.3349823","mag":"2971980782"},"language":"en","primary_location":{"id":"doi:10.1145/3349801.3349823","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3349801.3349823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Distributed Smart Cameras","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.03748","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Rao Muhammad Umer","orcid":null},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Rao Muhammad Umer","raw_affiliation_strings":["University of Udine, Udine, Italy"],"affiliations":[{"raw_affiliation_string":"University of Udine, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gian Luca Foresti","orcid":null},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gian Luca Foresti","raw_affiliation_strings":["University of Udine, Udine, Italy"],"affiliations":[{"raw_affiliation_string":"University of Udine, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]},{"author_position":"last","author":{"id":null,"display_name":"Christian Micheloni","orcid":null},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Christian Micheloni","raw_affiliation_strings":["University of Udine, Udine, Italy"],"affiliations":[{"raw_affiliation_string":"University of Udine, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I129043915"],"apc_list":null,"apc_paid":null,"fwci":1.0218,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.81063948,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":1.0,"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":1.0,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.996999979019165,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9959999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6769000291824341},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6212999820709229},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5548999905586243},{"id":"https://openalex.org/keywords/bicubic-interpolation","display_name":"Bicubic interpolation","score":0.507099986076355},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5052000284194946},{"id":"https://openalex.org/keywords/deblurring","display_name":"Deblurring","score":0.47699999809265137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4672999978065491},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.45590001344680786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.758899986743927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.677299976348877},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6769000291824341},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6212999820709229},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C49608258","wikidata":"https://www.wikidata.org/wiki/Q611705","display_name":"Bicubic interpolation","level":4,"score":0.507099986076355},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5052000284194946},{"id":"https://openalex.org/C2777693668","wikidata":"https://www.wikidata.org/wiki/Q25053743","display_name":"Deblurring","level":5,"score":0.47699999809265137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4672999978065491},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4641000032424927},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.45590001344680786},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3481000065803528},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.25529998540878296}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3349801.3349823","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3349801.3349823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Distributed Smart Cameras","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1909.03748","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.03748","pdf_url":"https://arxiv.org/pdf/1909.03748","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},{"id":"pmh:oai:air.uniud.it:11390/1168786","is_oa":false,"landing_page_url":"http://hdl.handle.net/11390/1168786","pdf_url":null,"source":{"id":"https://openalex.org/S4306401163","display_name":"Institutional Research Information System (University of Udine)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I129043915","host_organization_name":"University of Udine","host_organization_lineage":["https://openalex.org/I129043915"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.03748","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.03748","pdf_url":"https://arxiv.org/pdf/1909.03748","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1443987605","https://openalex.org/W1677182931","https://openalex.org/W1870456987","https://openalex.org/W1885185971","https://openalex.org/W1906770428","https://openalex.org/W1930824406","https://openalex.org/W1978749115","https://openalex.org/W1996726072","https://openalex.org/W2044945560","https://openalex.org/W2092663520","https://openalex.org/W2105561450","https://openalex.org/W2110158442","https://openalex.org/W2121058967","https://openalex.org/W2137801848","https://openalex.org/W2142058898","https://openalex.org/W2294652642","https://openalex.org/W2359099468","https://openalex.org/W2476548250","https://openalex.org/W2508457857","https://openalex.org/W2536599074","https://openalex.org/W2964277374","https://openalex.org/W4244393449","https://openalex.org/W4292363360"],"related_works":[],"abstract_inverted_index":{"Single":[0],"Image":[1],"Super-Resolution":[2],"(SISR)":[3],"aims":[4],"to":[5,156],"generate":[6],"a":[7,12,33,55,67,94,120],"high-resolution":[8],"(HR)":[9],"image":[10,35,53,69],"of":[11,19,42,62,66,85,103],"given":[13],"low-resolution":[14],"(LR)":[15],"image.":[16,45],"The":[17],"most":[18],"existing":[20],"convolutional":[21],"neural":[22],"network":[23,97],"(CNN)":[24],"based":[25],"SISR":[26,80,96,158],"methods":[27],"usually":[28],"take":[29],"an":[30,43,63,111],"assumption":[31],"that":[32,98,138],"LR":[34,52,68,132],"is":[36,54],"only":[37,143],"bicubicly":[38,56],"down":[39],"sampled":[40],"version":[41,61],"HR":[44,64],"However,":[46],"the":[47,51,72,79],"true":[48],"degradation":[49,151],"(i.e.":[50],"downsampled,":[57],"blurred":[58],"and":[59,106,134],"noisy":[60],"image)":[65],"goes":[70],"beyond":[71],"widely":[73],"used":[74],"bicubic":[75],"assumption,":[76],"which":[77,117],"makes":[78],"problem":[81],"highly":[82],"ill-posed":[83],"nature":[84],"inverse":[86],"problems.":[87],"To":[88],"address":[89],"this":[90],"issue,":[91],"we":[92],"propose":[93],"deep":[95],"works":[99],"for":[100,124],"blur":[101],"kernels":[102],"different":[104,107],"sizes,":[105],"noise":[108],"levels":[109],"in":[110],"unified":[112],"residual":[113],"CNN-based":[114,122],"denoiser":[115],"network,":[116],"significantly":[118],"improves":[119],"practical":[121,157],"super-resolver":[123],"real":[125,135],"applications.":[126,159],"Extensive":[127],"experimental":[128],"results":[129,147],"on":[130,148],"synthetic":[131],"datasets":[133],"images":[136],"demonstrate":[137],"our":[139],"proposed":[140],"method":[141],"not":[142],"can":[144],"produce":[145],"better":[146],"more":[149],"realistic":[150],"but":[152],"also":[153],"computational":[154],"efficient":[155]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-09-12T00:00:00"}
