  Recently, the deep convolutional neural network (CNN) has made remarkable
progress in single image super resolution(SISR). However, blindly using the
residual structure and dense structure to extract features from LR images, can
cause the network to be bloated and difficult to train. To address these
problems, we propose a simple and efficient distilling with residual
network(DRN) for SISR. In detail, we propose residual distilling block(RDB)
containing two branches, while one branch performs a residual operation and the
other branch distills effective information. To further improve efficiency, we
design residual distilling group(RDG) by stacking some RDBs and one long skip
connection, which can effectively extract local features and fuse them with
global features. These efficient features beneficially contribute to image
reconstruction. Experiments on benchmark datasets demonstrate that our DRN is
superior to the state-of-the-art methods, specifically has a better trade-off
between performance and model size.
