Deeply-Recursive Convolutional Network for Image Super-Resolution

作者：Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee · 年份：2016 · DOI：10.1109/cvpr.2016.181 · 被引用次数：3125 · 研究领域：Advanced Image Processing Techniques、Advanced Vision and Imaging、Image Processing Techniques and Applications

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.