Deeply-Recursive Convolutional Network for
Jung Kwon Lee
Kyoung Mu Lee
AbstractWe 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.
Jiwon Kim, Jung Kwon Lee and Kyoung Mu Lee, "Deeply-Recursive Convolutional Network for Image Super-Resolution", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Red color indicates the best performance and blue color indicates the second best performance.