For an image with a large damaged area, in the existing image restoration method, a distorted structure or a blurred texture that does not coincide with the surrounding area tends to be generated. With the development and application of deep learning, this paper is based on the method of generative adversarial networks and generates missing content by adjusting the available data. For a data set, the samples in the data set are first parsed into sample points in a probability distribution, a large number of falsified images are quickly generated using the generative adversarial network, the code of the closest damaged image is searched for, and then the code is generated by generating model to infer missing content. On this basis, this paper combines the semantic loss function and the perceptual loss function, and the unsaturated region is enlarged by improving the activation function sigmoid function, and the problem that the gradient easily disappears is solved. Experiments show that the method successfully predicts the information of large areas missing in the image, and realizes the photorealism, producing clearer and more consistent results than previous methods.