Inpainting Digital Dunhuang Murals with Structure-Guided Deep Network
Zhiheng Zhou, Xinran Liu, Junyuan Shang, Junchu Huang, Zhihao Li, Haiping Jia
Abstract
Inpainting deteriorated regions in digital Dunhuang murals is important for Dunhuang mural content preservation. Algorithms of mural image inpainting help simplify the digital restoration process of the deteriorated murals. Most of the existing algorithms can restore plausible content for homogeneous missing regions in Dunhuang mural images. However, they often fail to fill accurate color in missing regions that contain complex structures, which is mainly due to the neglect of color relevance between positions in the missing structural region and the non-missing color regions. In this article, we propose a deep learning–based, structure-guided inpainting method for the Dunhuang mural image, which utilizes relevant color information in deep features to improve the color inpainting quality for structural regions. Specifically, we design a structure-guided feature refinement module, which explicitly leverages color relevance implied in structure information to select relevant features for refining features in the missing region. In addition, we propose a multi-step scheme for feature refinement to better propagate non-missing region feature information to the missing region. We conduct experiments on Dunhuang660 and Dunhuang No. 7 Grotto datasets. The results demonstrate that our proposed method can achieve improved color inpainting quality for missing structural regions in Dunhuang mural images.