Image super-resolution has been a popular research topic in the field of image processing. It is a process of getting a high-resolution image from one or multiple low-resolution images to increase the number of pixels. Deep learning has been highly concerned by many fields in recent years, and all related fields have excellent results. Convolutional neural networks are widely used in computer vision and image recognition. In this thesis, adopt deep learning of convolutional neural network and full convolution network to do super-resolution, to explore the structure for the impact of resolution. Through the deconvolution method to understand the process of convolutional neural network and full convolution network, and then make effective adjustments to improve its image resolution, compared with the traditional interpolation method.