Improving Super-Resolution GAN Performance through Discriminator using U-Net Structure and Auxiliary Classifier

Dong Min Cheon, Younghwan Jeong, Wonsik Lee, Sounghyouk Wi, Sangjin Nam

http://doi.org/10.5626/JOK.2021.48.11.1194

In this paper, We propose a new super resolution method using a Generative Adversarial Network(GAN). Several super resolution techniques, including interpolation, CNN(Convolutional Neural Network), and GAN, have been proposed. Among them, GAN is the most preferred because of its good performance in image synthesis. Consequently, there have been many attempts to improve the super resolution quality by changing the network structure and loss function of GAN’s Generator, but the focus of improvement was not focused on the discriminator. The findings of the present study confirmed that the U-Net structure and the auxiliary classifier structure for image rotation, which were presented in other papers, had a positive effect on super-resolution network.