A comparative study on super resolution with deep learning

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dc.authorid0000-0002-1351-7565en_US
dc.contributor.authorTemiz, Hakan
dc.contributor.authorTüfekçi, Aslıhan
dc.contributor.authorBilge, Hasan Şakir
dc.date.accessioned2021-04-01T10:20:43Z
dc.date.available2021-04-01T10:20:43Z
dc.date.issued2018
dc.departmentAÇÜ, Borçka Acarlar Meslek Yüksekokuluen_US
dc.descriptionBook Series: Signal Processing and Communications Applications Conferenceen_US
dc.description.abstractDeep learning architectures are applied in the solution of many problems and give very successful results compared to other methods. One of these problems is the Super Resolution problem. In this study, we tried to solve the problem of super resolution by using different deep learning architectures to obtain higher resolution images. The models used in this study are focused on the images scaled up by factors of 2, 3 and 4. As a result of the experimental studies, the model success is increased as the network depth and samples are increased. Instead of a shallow model with more number of parameters, a deep model with lower number of parameters offers more successful results.
dc.description.abstractDeep learning architectures are applied in the solution of many problems and give very successful results compared to other methods. One of these problems is the Super Resolution problem. In this study, we tried to solve the problem of super resolution by using different deep learning architectures to obtain higher resolution images. The models used in this study are focused on the images scaled up by factors of 2, 3 and 4. As a result of the experimental studies, the model success is increased as the network depth and samples are increased. Instead of a shallow model with more number of parameters, a deep model with lower number of parameters offers more successful results
dc.description.sponsorshipIEEE; Huawei; Aselsan; NETAS; IEEE Turkey Sect; IEEE Signal Proc Soc; IEEE Commun Soc; ViSRATEK; Adresgezgini; Rohde & Schwarz; Integrated Syst & Syst Design; Atilim Univ; Havelsan; Izmir Katip Celebi Univen_US
dc.identifier.citationTemiz, H., Tüfekci, A., & Bilge, H. Ş. (2018). A comparative study on super resolution with deep learning. In 2018 26th Signal Processing and Communications Applications Conference (SIU), İzmir, TURKEYen_US
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11494/2897
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTemiz, Hakan
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof26th Signal Processing and Communications Applications Conference (SIU)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectSuper resolutionen_US
dc.subjectBicubic interpolationen_US
dc.subjectConvolutional neural networken_US
dc.subjectDerin öğrenmeen_US
dc.subjectSüper çözünürlüken_US
dc.subjectÇift kübik ara değerlemeen_US
dc.subjectKatlamalı sinir ağıen_US
dc.titleA comparative study on super resolution with deep learningen_US
dc.title.alternativeDerin öğrenme ile süper çözünürlük üzerine karşılaştırmalı bir çalışmaen_US
dc.typeConference Object

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