{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ancient-painting-to-natural-image-a-new","title":"Ancient Painting to Natural Image: A New Solution for Painting Processing","arxiv_id":"1901.00224","date":"2019-01-02","proceeding":null,"authors":["Tingting Qiao","Weijing Zhang","Miao Zhang","Zixuan Ma","Duanqing Xu"],"abstract":"Collecting a large-scale and well-annotated dataset for image processing has\nbecome a common practice in computer vision. However, in the ancient painting\narea, this task is not practical as the number of paintings is limited and\ntheir style is greatly diverse. We, therefore, propose a novel solution for the\nproblems that come with ancient painting processing. This is to use domain\ntransfer to convert ancient paintings to photo-realistic natural images. By\ndoing so, the ancient painting processing problems become natural image\nprocessing problems and models trained on natural images can be directly\napplied to the transferred paintings. Specifically, we focus on Chinese ancient\nflower, bird and landscape paintings in this work. A novel Domain Style\nTransfer Network (DSTN) is proposed to transfer ancient paintings to natural\nimages which employ a compound loss to ensure that the transferred paintings\nstill maintain the color composition and content of the input paintings. The\nexperiment results show that the transferred paintings generated by the DSTN\nhave a better performance in both the human perceptual test and other image\nprocessing tasks than other state-of-art methods, indicating the authenticity\nof the transferred paintings and the superiority of the proposed method.","url_abs":"http://arxiv.org/abs/1901.00224v2","url_pdf":"http://arxiv.org/pdf/1901.00224v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ancient-painting-to-natural-image-a-new","repo_url":"https://github.com/qiaott/AncientPainitng2NaturalImage","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}