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A comparative study on super resolution with deep learning

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info:eu-repo/semantics/closedAccess

Date

2018

Author

Temiz, Hakan
Tüfekçi, Aslıhan
Bilge, Hasan Şakir

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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

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.
 
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
 

Source

26th Signal Processing and Communications Applications Conference (SIU)

URI

https://hdl.handle.net/11494/2897

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  • Kontrol Ve Otomasyon Teknolojileri - Bildiriler & Sunumlar [1]
  • Scopus İndeksli Yayınlar Koleksiyonu [525]
  • WoS İndeksli Yayınlar Koleksiyonu [685]



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