Aiming at the problem that the current image super-resolution reconstruction method fails to make full use of the global and local information of the image, which causes the reconstruction result to lose part of the source image information to a certain extent, a multi-scale dense residual network is proposed to achieve this. Super-resolution reconstruction of the image. The network is based on dense residuals and integrates the multi-scale feature information of the image, ensuring that the network does not lose feature information in depth while obtaining more information under different receptive fields, thereby avoiding excessive loss of information in the original image. In addition, in order to recover high-resolution images containing enough high-frequency information from low-resolution images with low-frequency redundant information, the network combines spatial attention and channel attention to process low-resolution features at different scales in an unequal manner. This method can effectively highlight the high-frequency components in the feature map, so that the network can better learn and fit the feature information of the label image, and restore an image that is close to the real image. A large number of experimental results prove the effectiveness of this method.