Abstract: In order to preserve and restore the precious ancient mural art in a better way, based on the existing manual restoration technology, the digital virtual restoration method can effectively improve the efficiency of restoration and reduce the costs of restoration. In this aspect, using the generative network method in deep learning to automatically generate the missing part of the murals for completion and restoration can achieve good results. The network used for restoration is basically an autoencoder. The encoder takes the murals images to be processed and the mask corresponding to the damaged part as the input for feature extraction. The decoder will restore the feature chart obtained from the encoder to its original size by deconvolution, which completes the restoration. In this process, the damaged area will be completed automatically. At the same time, separating the murals into different pieces, restoring and reassembling them later makes it achievable to restore murals of any size. Compared with other digital mural restoration methods, the one proposed in the present study is applicable to more general purposes and not limited by the type of murals and their damage. In the generally damaged murals, this method can achieve a better restoration effect compared with the existing level. Moreover, even for a large-area damaged mural where the naked eyes cannot identify effective information, this method can nevertheless restore it to one containing images of full meaning.

Key words: murals repainting, convolutional neural network, generating model, autoencoder