A comparative study on super resolution with deep learning
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, TURKEYAbstract
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