Image restoration technology of Tang Dynasty tomb murals based on adversarial edge learning

Abstract Xi'an, China houses a vast collection of valuable Tang dynasty (618AD-907AD) tomb murals that have experienced various degrees of damage over time due to weathering. This research proposes an adversarial edge learning-based mural restoration technique that provides a fast and accurate automatic repair of the murals. The method uses generative adversarial networks to learn how to repair the edge contour of the damaged mural and restore the missing content. The contour restoration network and the mural restoration network are trained and updated simultaneously to enhance the feature extraction and restoration coordination performance of the network. The experiments were conducted on a self-built Tang dynasty tomb mural painting restoration dataset and compared to several existing algorithms. The proposed algorithm showed a 1.3 increase in Peak Signal to Noise Ratio (PSNR) and a 1.43% increase in Structural Similarity (SSIM) value, providing a precise restoration of the structure and contour of the mural content. The algorithm successfully repaired damaged murals with fractures and small parts breaking off. The suggested method can be useful for both the technical repair and conservation of significant national visual cultural property and the digital restoration of historic murals.