In this paper, we propose a single image super-resolution (SR) method based on frequency-dependent training of convolutional neural networks. Several researchers have focused on the reconstruction of super-resolution images by training a single convolutional neural network. In the proposed method, we divided the input images into three sub-frequency groups and then trained a convolutional neural network for each sub-frequency group. Then, the final output images were reconstructed by combining the SR images from the multiple networks. Experimental results show that the proposed training method produces promising performance.
|Title of host publication||Proceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 2020 Feb|
|Event||21st IEEE International Conference on Industrial Technology, ICIT 2020 - Buenos Aires, Argentina|
Duration: 2020 Feb 26 → 2020 Feb 28
|Name||Proceedings of the IEEE International Conference on Industrial Technology|
|Conference||21st IEEE International Conference on Industrial Technology, ICIT 2020|
|Period||20/2/26 → 20/2/28|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1D1A1B07050345).
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering