  Collecting a large-scale and well-annotated dataset for image processing has
become a common practice in computer vision. However, in the ancient painting
area, this task is not practical as the number of paintings is limited and
their style is greatly diverse. We, therefore, propose a novel solution for the
problems that come with ancient painting processing. This is to use domain
transfer to convert ancient paintings to photo-realistic natural images. By
doing so, the ancient painting processing problems become natural image
processing problems and models trained on natural images can be directly
applied to the transferred paintings. Specifically, we focus on Chinese ancient
flower, bird and landscape paintings in this work. A novel Domain Style
Transfer Network (DSTN) is proposed to transfer ancient paintings to natural
images which employ a compound loss to ensure that the transferred paintings
still maintain the color composition and content of the input paintings. The
experiment results show that the transferred paintings generated by the DSTN
have a better performance in both the human perceptual test and other image
processing tasks than other state-of-art methods, indicating the authenticity
of the transferred paintings and the superiority of the proposed method.
