Aiming at the shortcomings of current image restoration algorithms, such as discontinuity of repair effect, limitation of missing size, and instability of training process, an image restoration method based on generation antagonistic network is proposed. Using convolutional neural network, we can actually repair images of any resolution. In order to realize the real restoration effect of high resolution and the full learning of image features, we propose to obtain high resolution images based on the details and structure of DenseNet propagating source images, so as to realize the generation of missing images. As Iizuka et al. proposed the large computation amount generated by the expanded convolution part in the two-discriminator method, we propose to use JPU (Joint Pyramid Upsampling) to accelerate the calculation. Experiments in CelebA and ImageNet show that the proposed method can truly repair most broken images.