In this research, Dunhuang murals is taken as the object of restoration, and the role of digital repair combined with deep learning algorithm in mural restoration is explored. First, the image restoration technology is described, as well as its advantages and disadvantages are analyzed. Second, the deep learning algorithm based on artificial neural network is described and analyzed. Finally, the deep learning algorithm is integrated into the digital repair technology, and a mural restoration method based on the generalized regression neural network is proposed. The morphological expansion method and anisotropic diffusion method are used to preprocess the image. The MATLAB software is used for the simulation analysis and evaluation of the image restoration effect. The results show that in the restoration of the original image, the accuracy of the digital image restoration technology is not high. The nontexture restoration technology is not applicable in the repair of large-scale texture areas. The predicted value of the mural restoration effect based on the generalized neural network is closer to the true value. The anisotropic diffusion method has a significant effect on the processing of image noise. In the image similarity rate, the different number of training samples and smoothing parameters are compared and analyzed. It is found that when the value of δ is small, the number of training samples should be increased to improve the accuracy of the prediction value. If the number of training samples is small, a larger value of δ is needed to get a better prediction effect, and the best restoration effect is obtained for the restored image. Through this study, it is found that this study has a good effect on the restoration model of Dunhuang murals. It provides experimental reference for the restoration of later murals.

本研究以敦煌壁画为修复对象，探讨了数字修复与深度学习算法相结合在壁画修复中的作用。首先，介绍图像复原技术，并分析其优缺点。其次，对基于人工神经网络的深度学习算法进行了描述和分析。最后，将深度学习算法集成到数字修复技术中，提出了一种基于广义回归神经网络的壁画修复方法。使用形态学扩展方法和各向异性扩散方法对图像进行预处理。MATLAB软件用于图像恢复效果的仿真分析和评估。结果表明，在恢复原始图像时，数字图像恢复技术的准确性不高。无纹理恢复技术不适用于大范围纹理区域的修复。基于广义神经网络的壁画修复效果的预测值更接近真实值。各向异性扩散方法对图像噪声的处理具有重要影响。在图像相似率中，比较和分析了不同数量的训练样本和平滑参数。发现当δ的值小时，应增加训练样本的数目以提高预测值的准确性。如果训练样本的数量较少，则需要较大的δ值才能获得更好的预测效果，并且对于还原的图像可以获得最佳的还原效果。通过这项研究，发现该研究对敦煌壁画的修复模型具有很好的效果。它为以后壁画的修复提供了实验参考。