An accelerated image super-resolution reconstruction algorithm based on deep residual network is proposed to solve some existing problems, such as few convolutional layers, simple model, large amount of calculation, slow convergence speed and fuzzy image texture. This method improves image resolution and accelerates convergence speed at the same time. First, a deep convolutional neural network model is proposed to improve accuracy, and accelerate convergence of network models is achieved by residual learning and Adam optimization method. Second, feature mapping is performed directly on the original low-resolution image, and the sub-pixel convolutional layer is introduced at the end of the network to rearrange the pixels, so a high-resolution image is obtained. Experimental results show that the proposed algorithm has higher peak signal-to-noise ratio and structural similarity index than those of existing algorithms on set 5, set 14 and BSD100 test sets, and can recover more image details. The image edges are complete and the convergence speed is fast.