An Artistic Style Transfer Approach Incorporating Lightweight ViT and GAN
Aiming at the problems of insufficient local information extraction capability,low style transfer efficien-cy,and artifacts in current Vision Transformer(ViT)-based artistic style transfer methods,a lightweight ViT and adversarial generative network(GAN)combination method LVGAST is proposed.The method uses the comple-mentarity of local and global information to improve the inference efficiency and stylization quality,and enhance the artistic realism of the stylization results through adversarial training.Qualitative and quantitative comparative analysis with six other style transfer methods is carried out.The results show that:in qualitative aspect,LVGAST visual effect is more realistic in art;in quantitative terms,LVGAST reaches 0.499 and 1.452 in SSIM and Style loss,respectively,and achieves the fastest inference speed among the ViT-based methods(0.215s per piece).LVGAST combines the advantages of convolutional neural networks and ViTs to enhance stylization efficiency and introduces a discriminative network to achieve more artistically realistic stylization.