Aiming at the problems of network training difficulty and low utilization rate of feature information caused by increasing network layers in super-resolution restoration technology， an image super-resolution restoration algorithm based on dual attention Information Distillation Network （IDN） was designed and implemented. Firstly， by taking the advantage of the low computational complexity of IDN and the advantage of the information distillation module by which more features were extracted， the weights of the features were readjust adaptively by introducing the Residual Attention Module （RAM） and considering the interdependence of image channels， so as to further improve the reconstruction ability of high-resolution details of images. Then， a new mixed loss function sensitive to edge information was designed to refine the image and accelerate the convergence of the network. Test results on Set5， Set14， BSD100 and Urban100 public datasets show that the visual effect and Peak Signal-to-Noise Ratio （PSNR） of the proposed method are superior to those of the current mainstream algorithms.