Abstract: Chinese ancient murals are precious cultural heritage of humanity,recording the social,religious,cultural,and artistic activities of people in various regions of China throughout history.Due to prolonged exposure to the natural environment,many murals have developed defects such as cracks,scratches,corrosion,and even large-scale peeling.Therefore,the protection and restoration of murals are urgently needed.The digital restoration technology for damaged murals has become an important means to solve this problem by reconstructing the structure and texture of the mural images and virtually filling the damaged areas.Most existing mural image restoration methods are hard to effectively restore missing mural content with complex structures and rich color variations.In response to this issue,this paper proposes an ancient mural image restoration network using the involution cascade attention mechanism.The network firstly uses involution operations instead of traditional convolutions to improve the quality of repairing damaged mural textures and colors.Secondly,a cascaded attention module is proposed to capture contextual information at different scales,which can better repair damaged areas of murals of different sizes.Thirdly,FFC residual blocks are introduced to capture global structural information to enhance the network’s color restoration ability for damaged areas of murals.This article conducts experiments on simulated and real damaged mural datasets,comparing the restoration results with four other classic methods.The experimental results show that the proposed model outperforms other comparative methods in restoring the clarity,color consistency,and structural continuity of mural textures.

Key words: Ancient mural digital restoration, Cascading attention module, Deep learning, Fast Fourier Convolutional, Involution