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Recently, the deep convolutional neural network (CNN) has made remarkableprogress in single image super resolution(SISR). However, blindly using theresidual structure and dense structure to extract features from LR images, cancause the network to be bloated and difficult to train. To address theseproblems, we propose a simple and efficient distilling with residualnetwork(DRN) for SISR. In detail, we propose residual distilling block(RDB)containing two branches, while one branch performs a residual operation and theother branch distills effective information. To further improve efficiency, wedesign residual distilling group(RDG) by stacking some RDBs and one long skipconnection, which can effectively extract local features and fuse them withglobal features. These efficient features beneficially contribute to imagereconstruction. Experiments on benchmark datasets demonstrate that our DRN issuperior to the state-of-the-art methods, specifically has a better trade-offbetween performance and model size.

Distilling with Residual Network for Single Image Super Resolution