{"id":"https://openalex.org/W2902241747","doi":"https://doi.org/10.23919/eusipco.2018.8552961","title":"Effect of Training and Test Datasets on Image Restoration and Super-Resolution by Deep Learning","display_name":"Effect of Training and Test Datasets on Image Restoration and Super-Resolution by Deep Learning","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2902241747","doi":"https://doi.org/10.23919/eusipco.2018.8552961","mag":"2902241747"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2018.8552961","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8552961","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5015890477","display_name":"Og\u00fcn K\u0131rmemi\u015f","orcid":"https://orcid.org/0000-0002-7851-6352"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ogun Kirmemis","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey","institution_ids":["https://openalex.org/I1351752"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034648478","display_name":"A. Murat Tekalp","orcid":"https://orcid.org/0000-0003-1465-8121"},"institutions":[{"id":"https://openalex.org/I1351752","display_name":"Ko\u00e7 University","ror":"https://ror.org/00jzwgz36","country_code":"TR","type":"education","lineage":["https://openalex.org/I1351752"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"A. Murat Tekalp","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey","institution_ids":["https://openalex.org/I1351752"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1351752"],"apc_list":null,"apc_paid":null,"fwci":0.1521,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.5333678,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"514","last_page":"518"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9959999918937683,"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":0.9897000193595886,"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/computer-science","display_name":"Computer science","score":0.6782312393188477},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6321071982383728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6173921227455139},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.5936594009399414},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.5898610353469849},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5261912941932678},{"id":"https://openalex.org/keywords/superresolution","display_name":"Superresolution","score":0.5181671380996704},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48791906237602234},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3862107992172241},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34762340784072876},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33659955859184265},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.18402472138404846},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15328657627105713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6782312393188477},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6321071982383728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6173921227455139},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.5936594009399414},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.5898610353469849},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5261912941932678},{"id":"https://openalex.org/C141239990","wikidata":"https://www.wikidata.org/wiki/Q957423","display_name":"Superresolution","level":3,"score":0.5181671380996704},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48791906237602234},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3862107992172241},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34762340784072876},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33659955859184265},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.18402472138404846},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15328657627105713},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco.2018.8552961","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8552961","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:cdm21054.contentdm.oclc.org:IR/8543","is_oa":false,"landing_page_url":"http://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/8543","pdf_url":null,"source":{"id":"https://openalex.org/S4306401341","display_name":"Digital Collections portal (Ko\u00e7 University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1351752","host_organization_name":"Ko\u00e7 University","host_organization_lineage":["https://openalex.org/I1351752"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"Conference proceeding"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W1970185025","https://openalex.org/W2047920195","https://openalex.org/W2077028042","https://openalex.org/W2082789158","https://openalex.org/W2121927366","https://openalex.org/W2124964692","https://openalex.org/W2133665775","https://openalex.org/W2242218935","https://openalex.org/W2520164769","https://openalex.org/W2560533888","https://openalex.org/W2739757502","https://openalex.org/W2741137940","https://openalex.org/W2768546798","https://openalex.org/W2962790223","https://openalex.org/W2963312584","https://openalex.org/W2963470893","https://openalex.org/W2963996760","https://openalex.org/W2964046669","https://openalex.org/W2964121744","https://openalex.org/W4299408792","https://openalex.org/W4377561911","https://openalex.org/W6631190155","https://openalex.org/W6642769500","https://openalex.org/W6678856934","https://openalex.org/W6726381175"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W4375867731","https://openalex.org/W2365681766","https://openalex.org/W2943960151","https://openalex.org/W2393963626","https://openalex.org/W2049002029"],"abstract_inverted_index":{"Many":[0],"papers":[1,31],"have":[2],"recently":[3],"been":[4],"published":[5],"on":[6,62,158],"image":[7],"restoration":[8],"and":[9,21,68,76,99,119,130,137,140,154,176,193,199],"single-image":[10],"super-resolution":[11],"(SISR)":[12],"using":[13,102,115],"different":[14,87,97,159,169],"deep":[15,52],"neural":[16],"network":[17],"architectures,":[18],"training":[19,67,100,118,136,198],"methodology,":[20],"datasets.":[22],"The":[23],"standard":[24],"approach":[25],"for":[26],"performance":[27,56,84,106,167],"evaluation":[28],"in":[29,80,134],"these":[30],"is":[32,54,108,171],"to":[33,126],"provide":[34],"a":[35,48,103],"single":[36,104],"\u201caverage\u201d":[37],"mean-square":[38],"error":[39],"(MSE)":[40],"and/or":[41],"structural":[42],"similarity":[43],"index":[44],"(SSIM)":[45],"value":[46],"over":[47,183],"test":[49,69,92,120,138,160,185,200],"dataset.":[50],"Since":[51],"learning":[53],"data-driven,":[55],"of":[57,65,78,96,132,166,168,178,196],"the":[58,63,66,74,83,90,116,128,135,151,174,179,184,190,197],"proposed":[59,144],"methods":[60,101,170],"depends":[61],"size":[64],"sets":[70,201],"as":[71,73,187,189],"well":[72,149,188],"variety":[75,129],"complexity":[77,131,146,194],"images":[79,88,133],"them.":[81],"Furthermore,":[82],"varies":[85],"across":[86],"within":[89],"same":[91,117],"set.":[93],"Hence,":[94,163],"comparison":[95],"architectures":[98],"average":[105],"measure":[107],"difficult,":[109],"especially":[110],"when":[111],"they":[112],"are":[113,202],"not":[114],"sets.":[121,162],"We":[122],"propose":[123],"new":[124],"measures":[125,147,195],"characterize":[127],"sets,":[139],"show":[141],"that":[142],"our":[143],"dataset":[145],"correlate":[148],"with":[150],"mean":[152,175],"PSNR":[153],"SSIM":[155,182],"values":[156],"obtained":[157],"data":[161],"better":[164],"characterization":[165],"possible":[172],"if":[173],"variance":[177],"MSE":[180],"or":[181],"set":[186],"size,":[191],"resolution":[192],"specified.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
