Ancient Chinese painting style transfer based on CycleGAN

Abstract

Style transfer aims to alter the visual aesthetic of images by giving them a different artistic style. With the rapid advancement of deep learning, style transfer tasks have made significant progress, introducing new perspectives and innovative potentials within the realm of image processing. This study seeks to explore style transfer methods based on Cycle-Consistent Adversarial Networks (CycleGAN), enabling contemporary landscape photographs to take on the form of ancient Chinese paintings. This endeavor opens up fresh possibilities for artistic creation, image editing and design applications. The research encompasses an exposition of the process involved in constructing the CycleGAN model, alongside presenting research findings. Furthermore, it delves into the discussion of crucial techniques employed during the model training process, specifically the utilization of cycle consistency loss in configuring the loss functions. Lastly, this study ventures into future research directions, including strategies for further enhancing the performance and expanding the application scope of this style transfer model.