A single image super-resolution algorithm based on reversible network is proposed in this paper. Many models based on deep neural network (DNN) are proposed recently to solve the problem of single image super-resolution. In these models, the difference between the generated super-resolution image and the corresponding high-resolution image used to define the objective function and update the model parameters. These methods only take advantage of the dependence of high resolution image on low resolution image, and do not establish the inter-dependence between them. In this paper, we propose a super-resolution reversible network (SRRevnet) model based on reversible network to establish the Mutual mapping between the high-resolution image and low-resolution image. This model maps low resolution image and high resolution image to each other's resolution space respectively, and then use the error feedback to optimize those two opposite process. because the model is reversible, this model can optimize the process of super-resolution from forward and backward respectively. To our knowledge, this paper is the first to use the neural network with reversible structure to solve the problem of single image super- resolution. Through experiments, our model has achieved excellent results on the super-resolution benchmark datasets.