Most of the image super-resolution method based on convolutional neural network adopt end-to-end mode, which often has problems in reconstructing the image texture and missing high-frequency information. In order to solve this problem, a deep network structure based on super-resolution generation adversarial networks (SRGAN) for adversarial image edge learning was proposed. The edge information of the image was used to guide the super-resolution network to generate more real high-resolution images. The network model consists of two pairs of generative adversarial networks. First of all, using a generate adversarial network to generate high resolution edge feature map of the low resolution image , and then using the edge feature map to restrain and guide the second generate adversarial network to reconstruct the high resolution image. The experimental results on Set5, Set14, BSD100, Urban100 and Mangr109 benchmark dataset demonstrate that the reconstructed high-resolution image is more close to the real image, and has a good performance in terms of peak signal-to-noise ratio, structural similarity and perception index.