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

Red color indicates the best performance and blue color indicates the second best performance.