Ancient Painting to Natural Image: A New Solution for Painting Processing
This addresses the challenge of processing ancient paintings for computer vision applications, though it is incremental as it adapts existing domain transfer techniques to a specific domain.
The paper tackles the problem of limited and diverse ancient painting datasets by proposing a domain transfer solution that converts ancient paintings to photo-realistic natural images, allowing existing natural image processing models to be applied directly. Results show the transferred paintings outperform state-of-the-art methods in human perceptual tests and other image processing tasks.
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.