Research on transfer effect of ink style based on ChipGAN
Nan Liu, Hongjuan Wang, Yahui Ding · 2023
Generated Adversarial Network has been widely used in the field of image style transfer. Among them, ChipGAN is specifically aimed at the study of Chinese ink painting style transfer. In previous studies, it has demonstrated its excellent transfer effect at the visual level. In this paper, using CycleGAN and ChipGAN model, we adjust the parameters by controlling the variable method, quantitatively compare the image style transfer of the two models on the basis of visual effect comparison, and prove the effectiveness of ChipGAN in ink style transfer and the complexity in training.