dc.contributor.author | Temiz, Hakan | |
dc.contributor.author | Tüfekçi, Aslıhan | |
dc.contributor.author | Bilge, Hasan Şakir | |
dc.date.accessioned | 2021-04-01T10:20:43Z | |
dc.date.available | 2021-04-01T10:20:43Z | |
dc.date.issued | 2018 | en_US |
dc.identifier.citation | Temiz, 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, TURKEY | en_US |
dc.identifier.uri | https://hdl.handle.net/11494/2897 | |
dc.description | Book Series: Signal Processing and Communications Applications Conference | en_US |
dc.description.abstract | Deep 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. | en_US |
dc.description.abstract | Deep 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 | en_US |
dc.description.sponsorship | IEEE; 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 Univ | en_US |
dc.language.iso | tur | en_US |
dc.publisher | IEEE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Super resolution | en_US |
dc.subject | Bicubic interpolation | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Derin öğrenme | en_US |
dc.subject | Süper çözünürlük | en_US |
dc.subject | Çift kübik ara değerleme | en_US |
dc.subject | Katlamalı sinir ağı | en_US |
dc.title | A comparative study on super resolution with deep learning | en_US |
dc.title.alternative | Derin öğrenme ile süper çözünürlük üzerine karşılaştırmalı bir çalışma | en_US |
dc.type | conferenceObject | en_US |
dc.relation.journal | 26th Signal Processing and Communications Applications Conference (SIU) | en_US |
dc.department | AÇÜ, Borçka Acarlar Meslek Yüksekokulu | en_US |
dc.authorid | 0000-0002-1351-7565 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.contributor.institutionauthor | Temiz, Hakan | en_US |