Single Image Super-Resolution Based on Improved WGAN
- 10.2991/acaai-18.2018.24How to use a DOI?
- Super-resolution; WGAN-GP; VGG
SRGAN has successfully applied the Generative Adversarial Network to the single image super-resolution reconstruction, which has achieved good results. But the loss function based on feature space in SRGAN objectively sacrifices the pursuit of high peak signal-to-noise-ratio (PSNR), which is the result of a tradeoff. At the same time, Improved Training of Wasserstein GANs makes the training process more stable. We redesign the SRGAN, using VGG16 network for feature extraction, setting discriminator network's working space as feature space, and adding the loss function based on the mean square error of pixel space, then gain more details and high PSNR in the reconstruction at the same time. We use the design of WGAN-GP for reference to make the training more stable.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Lei Yu AU - Xiang Long AU - Chao Tong PY - 2018/03 DA - 2018/03 TI - Single Image Super-Resolution Based on Improved WGAN BT - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018) PB - Atlantis Press SP - 101 EP - 104 SN - 1951-6851 UR - https://doi.org/10.2991/acaai-18.2018.24 DO - 10.2991/acaai-18.2018.24 ID - Yu2018/03 ER -