Abstract
Abstract. Mural painting is one of the important cultural heritage reflecting the historical migration of the nation. In order to inherit these precious historical and cultural heritage, how to non - destructively and digitally protect and restore the existing murals has become an urgent task. The use of computer - assisted restoration of murals can not only save manpower and material resources, but also avoid secondary damage to the murals.However, most of the existing computer-assisted mural restoration algorithms use similar blocks with priority calculations and matching blocks in adjacent areas to guide mural restoration. There are some problems such as incoherent overall semantic structure, unnatural detail texture and inability to effectively repair large area missing remain to be solved. Aiming at the problems existing in the restoration of large area diseases such as paint loss and color fading in murals, we constructed a fine image restoration network model which based on generative adversarial network. A multi-scale dense matching repair network based on a generative adversarial network is constructed. First, the dense combination of dilated convolutions is used to improve the repair effect of detailed textures, Then, mean absolute Error, (Visual Geometry Group, VGG) feature matching, auto-guided regression, and geometric alignment are used as the loss function to guide the training of the generative network. Second, the discriminator with local and global branches is used to train the discriminant network, so that the repaired image is in the local and global content. Experiments were performed on the three mural data sets one by one. The results show that the network model can effectively restore the lines and faces in the murals. The images restored are not only coherent in semantic details, but also natural in color, which is conducive to the appreciation and display of murals. Thus, as one of the important directions of cultural heritage digital protection，the use of generative adversarial network in the digital restoration of ancient murals have been proved to be effective. It not only provides a reference for the true restoration of the murals but also means a lot to the preservation of murals.
Highlights
Due to the long-term natural weathering, man-made destruction and other factors, the murals suffered from various diseases such as paint loss and fading, which seriously affected the appearance and preservation of the murals
According to the core idea of algorithm, they can be divided into diffusion repair algorithms based on partial difference equations (PDE) (Tfc, A. , & Js, B. . 2001). and texture synthesis based similar patch group and filling algorithm (Criminisi, A. , P Pérez, & Toyama, K. . 2003). and iterative patch search and filling algorithm based on underlying features (Patch Match) (Barnes, C. . 2009)
The diffusion repair algorithm based on PDE uses the known information around the missing area to extend from the periphery of the missing area to the center according to the characteristics of isophotoes, so that the central pixels of the missing area can obtain coherence and realize image information restoration. based similar patch group and filling algorithm based on texture synthesis calculates the priority of the missing area and the known area to determine the best matching block, and fills it to the area to be repaired
Summary
Due to the long-term natural weathering, man-made destruction and other factors, the murals suffered from various diseases such as paint loss and fading, which seriously affected the appearance and preservation of the murals. Iterative patch search and fill algorithm based on underlying features, iteratively search for the optimal fill patch in the global image, and fill in the missing area to complete the repair. Used dilated convolution in the image repair network to obtain a larger receptive field They used local and global consistent confrontation training to generate similar repair results. In order to solve the limitations of the above methods, we propose a mural image restoration network model based on a generative network.
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