In recent years, deep convolutional neural networks have played an increasingly important role in single-image super-resolution (SR). However, with the increase of the depth and width of networks, the super-resolution methods based on convolution neural networks are facing training difficulties, memory consumption, running slowness and other problems. Furthermore, most of the methods do not make full use of the image gradient information which leads to the loss of geometric structure information of the image. To solve these problems, we propose a gradient information distillation network in this paper. On the one hand, the advantages of fast and lightweight are maintained through information distillation. On the other hand, the SR performance is improved by gradient information. Our network has two branches named gradient information distillation branch (GIDB) and image information distillation branch. To combine features in both branches, we also introduce a residual feature transfer mechanism (RFT). Under the function of GIDB and RFT, our network can retain the rich geometric structure information which can make the edge details of the reconstructed image sharper. The experimental results show that our method is superior to the existing methods while well limits the parameters, computation and running time of the model. It provides the possibility for real-time image processing and mobile applications.

近年来，深度卷积神经网络在单图像超分辨率（SR）中扮演着越来越重要的角色。然而，随着网络深度和宽度的增加，基于卷积神经网络的超分辨率方法面临训练困难，内存消耗，运行缓慢等问题。此外，大多数方法没有充分利用图像梯度信息，这导致图像的几何结构信息的丢失。为了解决这些问题，本文提出了一种梯度信息蒸馏网络。一方面，通过信息提炼保持了快速和轻量级的优势。另一方面，通过梯度信息改善了SR性能。我们的网络有两个分支，分别称为梯度信息蒸馏分支（GIDB）和图像信息蒸馏分支。为了在两个分支中合并特征，我们还引入了残差特征转移机制（RFT）。在GIDB和RFT的作用下，我们的网络可以保留丰富的几何结构信息，从而使重建图像的边缘细节更加清晰。实验结果表明，我们的方法优于现有方法，并且很好地限制了模型的参数，计算和运行时间。它为实时图像处理和移动应用程序提供了可能性。我们的网络可以保留丰富的几何结构信息，从而使重建图像的边缘细节更加清晰。实验结果表明，我们的方法优于现有方法，并且很好地限制了模型的参数，计算和运行时间。它为实时图像处理和移动应用程序提供了可能性。我们的网络可以保留丰富的几何结构信息，从而使重建图像的边缘细节更加清晰。实验结果表明，我们的方法优于现有方法，并且很好地限制了模型的参数，计算和运行时间。它为实时图像处理和移动应用程序提供了可能性。