The clarity of medical images directly affects the clinical diagnosis. Due to the limitations of imaging equipment and environmental factors， it is often impossible to directly obtain high-resolution images， and the hardware of most smart terminals is not suitable for running large-scale deep neural network models. Therefore， a lightweight dense neural network model with fewer layers and parameters was proposed. First of all， dense block and skip layer structure were used in the network for global and local image feature learning， and more feature information was introduced into the activation function， so that the shallow low-level image features in the network were able to be propagated to the high-layers more easily， thereby improving the super-resolution reconstruction quality of medical images. Then， the multi-stage method was adopted to train the network and the dual-task loss was used to strengthen the supervision and guidance in network learning， which solved the problem of difficulty increase in network training caused by highly magnified image super-resolution reconstruction. Compared with Nearest Neighbor （NN）， bilinear interpolation， bicubic interpolation， Convolutional Neural Network （CNN） based algorithm and the residual neural network based algorithm， the proposed model is of high practical value on better reconstructing the texture details of medical images， achieving higher Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM）， as well as achieving good result in both training speed and hardware consumption.