Restoration of non-structural damaged murals in Shenzhen Bao’an based on a generator–discriminator network

Abstract Shenzhen is a modern metropolis, but it hides a variety of valuable cultural heritage, such as ancient murals. How to effectively preserve and repair the murals is a worthy of discussion question. Here, we propose a generation-discriminator network model based on artificial intelligence algorithms to perform digital image restoration of ancient damaged murals. In adversarial learning, this study optimizes the discriminative network model. First, the real mural images and damaged images are spliced together as input to the discriminator network. The network uses a 5-layer encoder unit to down-sample the 1024 × 1024 × 3 image to 32 × 32 × 256. Then, we connect a layer of ZeroPadding2D to expand the image to 34 × 34 × 256, and pass the Conv2D layer, down-sample to 31 × 31 × 256, perform batch normalization, and repeat the above steps to get a 30 × 30 × 1 matrix. Finally, this part of the loss is emphasized in the loss function as needed to improve the texture detail information of the image generated by the Generator. The experimental results show that compared with the traditional algorithm, the PSNR value of the algorithm proposed in this paper can be increased by 5.86 db at most. The SSIM value increased by 0.13. Judging from subjective vision. The proposed algorithm can effectively repair damaged murals with dot-like damage and complex texture structures. The algorithm we proposed may be helpful for the digital restoration of ancient murals, and may also provide reference for mural restoration workers.