In order to realize the real-time and embedded operation of the model, a lightweight convolution neural network structure was proposed. By using a smaller filter size and introducing depthwise separable convolution, a large number of model parameters can be subtracted and the nonlinear capability can be improved. The sub-pixel convolution was introduced at the end of the network, then the mapping was learned directly from the original low-resolution image (without interpolation) to the high-resolution one, the cost is 1/k² as much as before (k is the magnification factor). Experimental results on Set5 show that the proposed model is more than 25.8 times faster than the classical super resolution reconstruction algorithm and can run in real-time on a common GPU; and the proposed method with only 35% parameters of SRCNN(super resolution convolution neural network), improves the PSNR(peak signal to noise ratio) with value of 0.17 dB.