Aiming at the existing mural inpating algorithms due to the lack of the ability to capture the remote features of the image,which leads to structural disorder in the inpating results,as well as the problem of inconsistent colour of the missing edges,a generative adversarial network mural restoration algorithm is proposed for the fusion of multiscale information.Firstly,the multi-branch dilated convolution architecture is introduced into the generative network,and the convolution kernel of each subdilated convolution locally expands the receptive field with different expansion rates to extract the local features of the image.Secondly,the fast Fourier convolution is combined to extract the features based on the global receptive field,so as to achieve the local-to-global feature extraction for the mural image.Finally,the self- attention and the PatchGAN discriminator are introduced to solve the problem of inconsistent colours of the missing edges.The model is trained and tested according to the homemade mural dataset,and compared with several groups of restoration algorithms for restoration.The experimental results show that compared with the comparison algorithms, the proposed algorithm improves PSNR by an average of 4.42 dB,SSIM by an average of 4.4%,and LPIPS by an average of 11.3%.The experiment proves that the proposed algorithm can effectively repair broken murals,and the repaired murals have better structure and texture information,and this method provides support for the restoration work of real murals.