Improving the effect of low-resolution face images output in AnimeGAN

Abstract

In this paper, a novel approach for improving the output cartoon-style effect of low-resolution face images in AnimeGAN is proposed, which is an useful and effective method to generate high-quality cartoon-style face images. This new approach I proposed combines generative adversarial networks (GAN), AnimeGAN and SRGAN. The previous existing methods do not give satisfactory results on processing low-resolution images. The cartoon-style images generated from the LR images have many significant visual issues. For example, the output cartoon-style faces have some unreasonable and weird shadows and wrinkles, which is unreal and far from the effect of original images. In this paper, I introduce a new method of using SRGAN to increase the resolution of the original LR images in order to improve the output effect in AnimeGAN. At last, the experimental results show that my combined method has good output from LR images and improves the cartoon-style faces effect as well as the performance of AnimeGAN.