/ESRGAN-PyTorch

A simple implementation of esrgan, which uses the pytorch framework.

Primary LanguagePythonApache License 2.0Apache-2.0

ESRGAN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.

Table of contents

Download weights

Download datasets

Contains DIV2K, DIV8K, Flickr2K, OST, T91, Set5, Set14, BSDS100 and BSDS200, etc.

Please refer to README.md in the data directory for the method of making a dataset.

How Test and Train

Both training and testing only need to modify the rrdbnet_config.py or esrgan_config.py file.

Test

Modify the esrgan_config.py file.

  • line 32: g_arch_name change to rrdbnet_x4.
  • line 39: upscale_factor change to 4.
  • line 41: mode change to test.
  • line 43: exp_name change to test_ESRGAN_x4.
  • line 100: g_model_weights_path change to ./results/pretrained_models/ESRGAN_x4-DFO2K-25393df7.pth.tar.
python3 test.py

Train RRDBNet model

modify the rrdbnet_config.py

  • line 31: g_arch_name change to rrdbnet_x4.
  • line 38: upscale_factor change to 4.
  • line 40: mode change to train.
  • line 43: exp_name change to RRDBNet_x4.
python3 train_rrdbnet.py

Resume train RRDBNet model

modify the rrdbnet_config.py

  • line 31: g_arch_name change to rrdbnet_x4.
  • line 38: upscale_factor change to 4.
  • line 40: mode change to train.
  • line 43: exp_name change to RRDBNet_x4.
  • line 59: resume_g_model_weights_path change to ./samples/RRDBNet_x4/g_epoch_xxx.pth.tar.
python3 train_rrdbnet.py

Train ESRGAN model

modify the esrgan_config.py

  • line 32: g_arch_name change to rrdbnet_x4.
  • line 39: upscale_factor change to 4.
  • line 41: mode change to test.
  • line 43: exp_name change to ESRGAN_x4.
  • line 58: pretrained_g_model_path change to ./results/RRDBNet_x4/g_best.pth.tar.
python3 train_esrgan.py

Resume train ESRGAN model

modify the esrgan_config.py

  • line 32: g_arch_name change to rrdbnet_x4.
  • line 39: upscale_factor change to 4.
  • line 41: mode change to test.
  • line 43: exp_name change to ESRGAN_x4.
  • line 61: resume_d change to ./samples/ESRGAN_x4/g_epoch_xxx.pth.tar.
  • line 62: resume_g change to ./samples/ESRGAN_x4/g_epoch_xxx.pth.tar.
python3 train_esrgan.py

Result

Source of original paper results: https://arxiv.org/pdf/1809.00219v2.pdf

In the following table, the value in () indicates the result of the project, and - indicates no test.

Method Scale Set5 (PSNR/SSIM) Set14 (PSNR/SSIM) BSD100 (PSNR/SSIM) Urban100 (PSNR/SSIM) Manga109 (PSNR/SSIM)
RRDB 4 32.73(32.71)/0.9011(0.9018) 28.99(28.96)/0.7917(0.7917) 27.85(27.85)/0.7455(0.7473) 27.03(27.03)/0.8153(0.8156) 31.66(31.60)/0.9196(0.9195)
ESRGAN 4 -(30.44)/-(0.8525) -(26.28)/-(0.6994) -(25.33)/-(0.6534) -(24.36)/-(0.7341) -(29.42)/-(0.8597) 
# Download `ESRGAN_x4-DFO2K-25393df7.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md<Download weights>`
python3 ./inference.py

Input:

Output:

Build `rrdbnet_x4` model successfully.
Load `rrdbnet_x4` model weights `./results/pretrained_models/ESRGAN_x4-DFO2K-25393df7.pth.tar` successfully.
SR image save to `./figure/baboon_sr.png`

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Credit

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang

Abstract
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge. The code is available at this https URL.

[Paper] [Author's implements(PyTorch)]

@misc{wang2018esrgan,
    title={ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks},
    author={Xintao Wang and Ke Yu and Shixiang Wu and Jinjin Gu and Yihao Liu and Chao Dong and Chen Change Loy and Yu Qiao and Xiaoou Tang},
    year={2018},
    eprint={1809.00219},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}