  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.
