The recent advances in deep convolutional neural networks (DCNNs) have convincingly demonstrated high-capability reconstruction for single image super-resolution (SR). However, it is a big challenge for most DCNNs-based SR models when the scaling factor increases. In this paper, we propose a novel Progressive Residual Network (PRNet) to integrate hierarchical and scale features for single image SR, which works well for both small and large scaling factors. Specifically, we introduce a Progressive Residual Module (PRM) to extract local multi-scale features through dense connected up-sampling convolution layers. Meanwhile, by embedding residual learning into each module, the relative information between high-resolution and low-resolution multi-scale features is fully exploited to boost reconstruction performance. Finally, the scale-specific features are fused to the reconstruction module for restoring the high-quality image. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate that our PRNet achieves superior performance and in particular obtains new state-of-the-art results for large scaling factors such as 4 × and 8 ×.

深度卷积神经网络（DCNN）的最新进展令人信服地证明了单图像超分辨率（SR）的高能力重建。但是，当比例因子增加时，对于大多数基于DCNN的SR模型来说，这是一个巨大的挑战。在本文中，我们提出了一种新颖的渐进式残差网络（PRNet），它将单个图像SR的层次结构和缩放功能集成在一起，这对于大小比例缩放因子均适用。具体来说，我们引入了渐进残差模块（PRM），以通过密集的连接的上采样卷积层提取局部多尺度特征。同时，通过将残差学习嵌入到每个模块中，可以充分利用高分辨率和低分辨率多尺度特征之间的相对信息来提高重建性能。最后，将比例尺特定的特征融合到重建模块以恢复高质量图像。在基准数据集上进行的大量定量和定性评估表明，我们的PRNet具有出色的性能，尤其是对于诸如4×和8×的大比例缩放因子，获得了最新的最新结果。