  We propose an image super-resolution method (SR) using a deeply-recursive
convolutional network (DRCN). Our network has a very deep recursive layer (up
to 16 recursions). Increasing recursion depth can improve performance without
introducing new parameters for additional convolutions. Albeit advantages,
learning a DRCN is very hard with a standard gradient descent method due to
exploding/vanishing gradients. To ease the difficulty of training, we propose
two extensions: recursive-supervision and skip-connection. Our method
outperforms previous methods by a large margin.
