Deeply-Recursive Convolutional Network for

Image Super-Resolution

Authors

Jiwon Kim

Jung Kwon Lee

Kyoung Mu Lee



Abstract

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.

Paper

pdf



Slide

PPT



Results

zip



Train/Test Data

zip(train)

zip(test)



Code

zip



Citation

Bibtex

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.



Qualitative results

Benchmark results