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EDSR (CVPR'2017)

Enhanced Deep Residual Networks for Single Image Super-Resolution

Task: 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 .

Model Dataset PSNR SSIM Training Resources Download
edsr_x2c64b16_1x16_300k_div2k Set5 35.7592 0.9372 1 model | log
edsr_x2c64b16_1x16_300k_div2k Set14 31.4290 0.8874 1 model | log
edsr_x2c64b16_1x16_300k_div2k DIV2K 34.5896 0.9352 1 model | log
edsr_x3c64b16_1x16_300k_div2k Set5 32.3301 0.8912 1 model | log
edsr_x3c64b16_1x16_300k_div2k Set14 28.4125 0.8022 1 model | log
edsr_x3c64b16_1x16_300k_div2k DIV2K 30.9154 0.8711 1 model | log
edsr_x4c64b16_1x16_300k_div2k Set5 30.2223 0.8500 1 model | log
edsr_x4c64b16_1x16_300k_div2k Set14 26.7870 0.7366 1 model | log
edsr_x4c64b16_1x16_300k_div2k DIV2K 28.9675 0.8172 1 model | log

Quick Start

Train

Train Instructions

You can use the following commands to train a model with cpu or single/multiple GPUs.

# cpu train
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py

# single-gpu train
python tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py

# multi-gpu train
./tools/dist_train.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py 8

For more details, you can refer to Train a model part in train_test.md.

Test

Test Instructions

You can use the following commands to test a model with cpu or single/multiple GPUs.

# cpu test
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth

# single-gpu test
python tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth

# multi-gpu test
./tools/dist_test.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth 8

For more details, you can refer to Test a pre-trained model part in train_test.md.

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}
}