In view of existing problems of image super-resolution method based on sample-learning, which is difficult to operate rapidly and generate high quality image at the same time, a rapid image super-resolution method based on deconvolution is proposed. A new type of network model is designed and low resolution images are taken as input images directly, and then convolution layer is used to extract and represent features. Deconvolution layer is used to enlarge image feature maps, and the following pooling layer is used to concentrate the feature maps and extract features which are more sensitive to the results. Moreover, sub-pixel convolution layer is applied to features mapping and images fusion simultaneously and the super-resolution image could be obtained. The proposed method is tested on images of test datasets, and compared with other methods. The test results of the proposed method have higher peak signal to noise ratio (PSNR) and can process more than 24 images in size of 320 pixel×240 pixel per second, which shows that the proposed rapid image super-resolution method based on deconvolution can not only generate images with higher quality, but also satisfy the requirement of real-time video processing.