Abstract
AbstractAncient murals are precious cultural heritages. They suffer from various damages due to man-made destruction and long-time exposure to the environment. It is urgent to protect and restore the damaged ancient murals. Virtual restoration of ancient murals aims to fill damaged mural regions by using modern computer techniques. Most existing restoration approaches fail to fill the loss mural regions with rich details and complex structures. In this paper, we propose a virtual restoration network of ancient murals based on global–local feature extraction and structural information guidance (GLSI). The proposed network consists of two major sub-networks: the structural information generator (SIG) and the image content generator (ICG). In the first sub-network, SIG can predict the structural information and the coarse contents for the missing mural regions. In the second sub-network, ICG utilizes the predicted structural information and the coarse contents to generate the refined image contents for the missing mural regions. Moreover, we design an innovative BranchBlock module that can effectively extract and integrate the local and global features. We introduce a Fast Fourier Convolution (FFC) to improve the color restoration for the missing mural regions. We conduct experiments over simulated and real damaged murals. Experimental results show that our proposed method outperforms other three comparative state-of-the-art approaches in terms of structural continuity, color harmony and visual rationality of the restored mural images. In addition, the mural restoration results of our method can achieve comparatively high quantitative evaluation metrics.
Funder
National Natural Science Foundation of China
Applied Basic Research Project of Yunnan Province, China
Publisher
Springer Science and Business Media LLC
Subject
Archeology,Archeology,Conservation,Computer Science Applications,Materials Science (miscellaneous),Chemistry (miscellaneous),Spectroscopy
Reference32 articles.
1. Guo D, Liang Y. Research on modeling characteristics and composition forms of Dunhuang mural art in Tang Dynasty: Research Institute of Management Science and Industrial Engineering. In: Proceedings of 2017 2nd international conference on education, sports, arts and management engineering (ICESAME 2017). Atlantis Press; 2017. 4. (in Chinese with an English abstract).
2. Liang Y, Guo D. Research on the color representation of Dunhuang mural art. In: Proceedings of the 2017 2nd international conference on education, sports, arts and management engineering. 2017 (in Chinese with an English abstract).
3. Bertalmio M, Sapiro G, Caselles V, Ballester C. Image inpainting. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques; 2000. p. 417–24.
4. Cheng Y, Ai Y, Guo H. Inpainting algorithm for Dunhuang mural based on improved curvature-driven diffusion model. J Comput-Aid Des Comput Graph. 2020;32(05):787–96 (in Chinese with an English abstract).
5. Criminisi A, Perez P, Toyama K. Object removal by exemplar-based inpainting. In: 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings, vol 2. p. II–II.