Convolutional neural networks have recently been shown to have the highest accuracy for single image super-resolution (SISR) reconstruction. Most of the network structures suffer from low training and reconstruction speed, and still have the problem that one model can only be rebuilt for a single scale. For these problems, a deep cascaded network (DCN) is designed to reconstruct the image step by step. L2 and the perception loss function are used to optimize the network together, and then a high quality reconstructed image will be obtained under the joint action of each cascade. In addition, our network can get reconstructions of different scales, such as 1.5′, 2′, 2.5′, 3′, 3.5′ and 4′. Extensive experiments on several of the largest benchmark datasets demonstrate that the proposed approach performs better than existing methods in terms of accuracy and visual improvement.