BSRGAN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .

Table of contents

• BSRGAN-PyTorch
  • Overview
  • Table of contents
  • Download weights
  • Download datasets
  • How Test and Train
    • Test BSRGAN_x4 model
    • Train BSRNet_x4 model
    • Resume train BSRNet_x4 model
    • Train BSRGAN_x4 model
    • Resume train BSRGAN_x4 model
  • Result
  • Contributing
  • Credit
    • Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

Download weights

• Google Driver
• Baidu Driver

Download datasets

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

• Google Driver
• Baidu Driver

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 bsrnet_config.py or bsrgan_config.py file.

Test BSRGAN_x4 model

Modify the bsrgan_config.py file.

• line 48: g_model_arch_name change to bsrgan_x4.
• line 60: upscale_factor change to 4.
• line 62: mode change to test.
• line 64: exp_name change to BSRGAN_x4-Set5.
• line 118: lr_dir change to f"./data/Set5/LRbicx{upscale_factor}".
• line 119: gt_dir change to f"./data/Set5/GTmod12".
• line 121: g_model_weights_path change to ./results/pretrained_models/BSRGAN_x4-DIV2K-6d507222.pth.tar.

python3 test.py

Train BSRNet_x4 model

Modify the bsrnet_config.py file.

• line 48: g_model_arch_name change to bsrgan_x4.
• line 60: upscale_factor change to 4.
• line 62: mode change to train.
• line 64: exp_name change to BSRNet_x4-DIV2K.

python3 train_bsrnet.py

Resume train BSRNet_x4 model

Modify the bsrnet_config.py file.

• line 48: g_model_arch_name change to bsrgan_x4.
• line 60: upscale_factor change to 4.
• line 62: mode change to test.
• line 64: exp_name change to BSRNet_x4-DIV2K.
• line 74: resume_g_model_weights_path change to ./samples/BSRNet_x4-DIV2K/g_epoch_xxx.pth.tar.
• line 118: lr_dir change to f"./data/Set5/LRbicx{upscale_factor}".
• line 119: gt_dir change to f"./data/Set5/GTmod12".

python3 train_bsrnet.py

Train BSRGAN_x4 model

Modify the bsrgan_config.py file.

• line 47: d_model_arch_name change to discriminator_unet.
• line 48: g_model_arch_name change to bsrgan_x4.
• line 60: upscale_factor change to 4.
• line 62: mode change to train.
• line 64: exp_name change to BSRGAN_x4-DIV2K.

python3 train_bsrgan.py

Resume train BSRGAN_x4 model

Modify the bsrgan_config.py file.

• line 47: d_model_arch_name change to discriminator_unet.
• line 48: g_model_arch_name change to bsrgan_x4.
• line 60: upscale_factor change to 4.
• line 62: mode change to train.
• line 64: exp_name change to BSRGAN_x4-DIV2K.
• line 83: resume_d_model_weights_path change to ./samples/BSRGAN_x4-DIV2K/d_epoch_xxx.pth.tar.
• line 84: resume_g_model_weights_path change to ./samples/BSRGAN_x4-DIV2K/g_epoch_xxx.pth.tar.
• line 118: lr_dir change to f"./data/Set5/LRbicx{upscale_factor}".
• line 119: gt_dir change to f"./data/Set5/GTmod12".

python3 train_bsrgan.py

Result

Source of original paper results: https://arxiv.org/pdf/2103.14006v2.pdf

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

• RealSRSet

Model	Scale	NIQE
BSRNet	2	-(7.25)
BSRGAN	2	-(6.39)
BSRNet	4	-(8.06)
BSRGAN	4	5.60(5.50)

# Download `BSRGAN_x4-DIV2K-6d507222.pth.tar` weights to `./results/pretrained_models`
# More detail see `README.md<Download weights>`
python3 ./inference.py

Input:

Output:

Build `bsrgan_x4` model successfully.
Load `bsrgan_x4` model weights `./results/pretrained_models/BSRGAN_x4-DIV2K-6d507222.pth.tar` successfully.
SR image save to `./figure/oldphoto2_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

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu

Abstract
It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse degradations of real images. To address this issue, this paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations. Specifically, the blur is approximated by two convolutions with isotropic and anisotropic Gaussian kernels; the downsampling is randomly chosen from nearest, bilinear and bicubic interpolations; the noise is synthesized by adding Gaussian noise with different noise levels, adopting JPEG compression with different quality factors, and generating processed camera sensor noise via reverse-forward camera image signal processing (ISP) pipeline model and RAW image noise model. To verify the effectiveness of the new degradation model, we have trained a deep blind ESRGAN super-resolver and then applied it to super-resolve both synthetic and real images with diverse degradations. The experimental results demonstrate that the new degradation model can help to significantly improve the practicability of deep super-resolvers, thus providing a powerful alternative solution for real SISR applications.

[Paper] [Code]

@inproceedings{zhang2021designing,
    title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},
    author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},
    booktitle={IEEE International Conference on Computer Vision},
    pages={4791--4800},
    year={2021}
}