Research advanced in image style transfer based on deep learning

Abstract

Image style transfer aims to change a picture’s style while maintaining the integrity of the image’s original information. Image style transfer techniques based on deep learning have expanded in variety as a result of the quick advancement of artificial intelligence technology, and have made breakthroughs in both transfer accuracy and visualization effect. Focusing on image style transfer based on depth learning, this paper systematically introduces its latest research progress. Specifically, according to the difference of network structure, this paper introduces four representative style transfer methods based on CNN and GAN, including their design ideas, process based, advantages and disadvantages. In addition, this paper also qualitatively and quantitatively compares the transfer results of different algorithms on different datasets. For GAN based method, AnimeGAN has a good conversion effect in style conversion. For the CNN based method, the combination of perceptual loss and feedforward network using perceptual loss is more prominent. Finally, this paper reviews the development and breakthrough of neural network style, and sorts out and discusses the differences and shortcomings of these technologies as well as some unresolved problems.