利用深度学习进行超分辨重建已经获得了极大的成功，但是目前绝大多数网络结构依然存在训练以及重建速度较慢，一个模型仅能重建一个尺度以及重建图像过于平滑等问题。针对这些问题，本文设计了一种级联的网络结构(DCN)来逐级对图像进行重建。使用L2 和感知损失函数共同优化网络，在每一级的共同作用下得到了最终高质量的重建图像。此外，本文的方法可以同时重建多个尺度，比如4′的模型可以重建1.5′，2′，2.5′，3′，3.5′，4′。在几个常用数据集上的实验表明，该方法在准确性和视觉效果均优于现有的方法。

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.