Convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SR). However, most previous methods predict high-resolution (HR) images in the spatial domain, producing over-smoothed outputs while losing texture details. To address this problem, in this paper we propose to predict nonsubsampled shearlet transform (NSST) coefficients, which better represent the global topology information and local texture details of HR images. On the other hand, we propose a deep hybrid resolution network by a residual-in-residual style, which aggregates features of multiple resolutions so as to gather rich context information in compact representations. When evaluated on a newly released RealSR dataset and traditional simulated datasets, our method, namely hybrid resolution NSST prediction (HRNP), achieves more appealing results, w.r.t. PSNR and SSIM, than the state-of-the-art methods. Moreover, we find our HRNP is more capable of preserving complex edges and curves than other methods.

卷积神经网络（CNN）在单图像超分辨率（SR）中取得了巨大的成功。但是，大多数以前的方法会在空间域中预测高分辨率（HR）图像，从而产生过度平滑的输出，同时会丢失纹理细节。为了解决这个问题，本文提出了预测非下采样的小波变换（NSST）系数的方法，该系数可以更好地表示HR图像的全局拓扑信息和局部纹理细节。另一方面，我们提出了一种残差形式的深度混合分辨率网络，该网络聚合了多种分辨率的特征，从而以紧凑的表示形式收集了丰富的上下文信息。在新发布的RealSR数据集和传统的模拟数据集上进行评估时，我们的方法（即混合分辨率NSST预测（HRNP））可获得更有吸引力的结果，与最新技术相比，它具有PSNR和SSIM的优势。此外，我们发现我们的HRNP比其他方法更能保留复杂的边缘和曲线。