Abstract: A proposed solution to the problem of high manual inpainting costs for ancient murals that have undergone varying degrees of natural weathering resulting in cracks,peeling,and other damage is to use a generative adversarial network with a framework based on fast Fourier convolution and coordinate attention.Most existing methods for mural inpainting have complex frameworks that consume a lot of computing power,and produce results that are inaccurate and of low quality.The proposed method takes the damaged mural image and mask as inputs to the network.They are then passed through an encoder and a residual module for feature inference to determine the reasonable content of the damaged area.During training,a specific discriminator that is used for inpainting tasks conducts adversarial training.Eventually,the desired inpainting effect is achieved.The feature inference portion of the proposed model consists of a residual block containing gate-controlled residual connections,six fast Fourier convolution modules,and an improved coordinate attention module for feature pruning.It has a large receptive field and the ability to extract rich features,which can solve the problem of poor inpainting results associated with current methods.Experimental results on a self-made dataset show that the proposed algorithm not only has a simpler structure but also outperforms several classic inpainting methods.Therefore,it can be applied to the inpainting of ancient murals and can save a significant amount of manual labor costs.

Key words: Mural inpainting, Attention mechanism, Residual network, Generative adversarial network, Deep learning