mmediting/configs/restorers/edsr

• EDSR (CVPR'2017)
• • Abstract
  • Results and models
  • Citation

EDSR (CVPR'2017)

Enhanced Deep Residual Networks for Single Image Super-Resolution

Abstract

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.

Results and models

Evaluated on RGB channels, scale pixels in each border are cropped before evaluation.
The metrics are PSNR / SSIM .

Method	Set5	Set14	DIV2K	Download
edsr_x2c64b16_1x16_300k_div2k	35.7592 / 0.9372	31.4290 / 0.8874	34.5896 / 0.9352	model | log
edsr_x3c64b16_1x16_300k_div2k	32.3301 / 0.8912	28.4125 / 0.8022	30.9154 / 0.8711	model | log
edsr_x4c64b16_1x16_300k_div2k	30.2223 / 0.8500	26.7870 / 0.7366	28.9675 / 0.8172	model | log

Citation

@inproceedings{lim2017enhanced,
  title={Enhanced deep residual networks for single image super-resolution},
  author={Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages={136--144},
  year={2017}
}