Deep convolutional networks have attracted great attention in image restoration and enhancement. Generally, restoration quality has been improved by building more and more convolutional block. However, these methods mostly learn a specific model to handle all images and ignore difficulty diversity. In other words, an area in the image with high frequency tend to lose more information during compressing while an area with low frequency tends to lose less. In this article, we adrress the efficiency issue in image SR by incorporating a patch-wise rolling network(PRN) to content-adaptively recover images according to difficulty levels. In contrast to existing studies that ignore difficulty diversity, we adopt different stage of a neural network to perform image restoration. In addition, we propose a rolling strategy that utilizes the parameters of each stage more flexible. Extensive experiments demonstrate that our model not only shows a significant acceleration but also maintain state-of-the-art performance.

翻译：一般而言,通过建立越来越多的进化区块提高了恢复质量。然而,这些方法大多学习一种处理所有图像的具体模型,忽略了困难的多样性。换句话说,高频图像中的一个区域在压缩时会丢失更多信息,而低频区域则会损失较少。在本篇文章中,我们通过根据困难程度将一个宽巧的滚动网络(PRN)纳入内容恢复图像,将效率问题压缩到图像SR中。与现有研究相比,我们采用不同阶段的神经网络来进行图像恢复。此外,我们建议采用更灵活的滚动战略,利用每个阶段的参数。广泛的实验表明,我们的模型不仅显示显著的加速,而且保持最先进的性能。