The image super-resolution method based on neural network often has the problem of reconstruct the image texture structure and missed high-frequency information. To address the above problems, a multi-scale parallel learning generation adversarial network structure is proposed by SRGAN. The generator consists of two different scale residual network blocks. First, the extracted low-resolution images pass through two sub-networks by Multi-scale feature learning, then use the fusion network for residual fusion, merge high-frequency information of different scales, and finally generate high-resolution images. The experimental results on Set5, Set14, BSD100 benchmark dataset and SpaceNet satellite image dataset proved that the algorithm has good effect in recovering the detailed texture information of low resolution images.