An improved image super-resolution algorithm based on the convolutional neural network is proposed to overcome many problems such as more parameters, a large amount of calculation, longer training time and fuzzy texture combined with the present image classification network model and visual recognition algorithms. The proposed algorithm adjusts the convolution kernel size to reduce parameters in the original three layers of convolutional neural network. Pool layers are added to reduce the dimension and decrease the computational complexity. The learning rate and size of input sub-blocks are improved to reduce the training time. The training database is expanded to provide extensive and comprehensive characteristics. Experimental results show that the proposed algorithm achieves good super-resolution results, and the subjective visual effect and objective evaluation indices are both improved obviously. The image resolution and edge sharpness are enhanced significantly.