Artistic style image migration model based on cycle-consistent generative adversarial networks

Art style image migration has been increasingly important in image processing research as computer vision and deep learning technologies develop. Most well-known style migration techniques depend on paired training data, which is occasionally difficult to get in practice. Many solutions for complex art forms lose material or have style inconsistencies, which makes it challenging to fulfil high-quality content preservation. Regarding the mentioned problems, this work presents ArtCycleGAN, a cycle-consistent generative adversarial network-based art style image migration model. Pre-trained VGG19 network perceptual loss and cycle-consistent loss help to enable high quality unsupervised art style migration. ArtCycleGAN proves its validity in art style migration by experimental findings showing good performance in style similarity, content retention, and perceptual quality. This work presents a dependable and efficient approach for unsupervised art style migration as well as fresh ideas and references for picture synthesis applied with generative adversarial networks.