Deeply Recursive Convolutional Network for Image Super Resolution
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.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)