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Real-ESRGAN (ICCVW'2021)

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Task: Image Super-Resolution

Abstract

Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. Specifically, a high-order degradation modeling process is introduced to better simulate complex real-world degradations. We also consider the common ringing and overshoot artifacts in the synthesis process. In addition, we employ a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics. Extensive comparisons have shown its superior visual performance than prior works on various real datasets. We also provide efficient implementations to synthesize training pairs on the fly.

Results and models

Evaluated on Set5 dataset with RGB channels. The metrics are PSNR and SSIM.

Model Dataset PSNR SSIM Training Resources Download
realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost df2k_ost 28.0297 0.8236 4 (Tesla V100-SXM2-32GB) model/log
realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost df2k_ost 26.2204 0.7655 4 (Tesla V100-SXM2-32GB) 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/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py

# single-gpu train
python tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py

# multi-gpu train
./tools/dist_train.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.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/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth

# single-gpu test
python tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth

# multi-gpu test
./tools/dist_test.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth 8

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

Citation

@inproceedings{wang2021real,
  title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic data},
  author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
  pages={1905--1914},
  year={2021}
}