Real_ESRGAN-PyTorch

Table of contents

• Real_ESRGAN-PyTorch
  • Introduction
  • Getting Started
    • Requirements
    • Local Install
  • All pretrained model weights
  • Inference
  • Inference video
  • Train
  • Result
  • Contributing
  • Credit
    • Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Introduction

This repository contains an op-for-op PyTorch reimplementation of Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

Getting Started

Requirements

• Python 3.10+
• PyTorch 2.0.0+
• CUDA 12.1+
• Ubuntu 22.04+

Local Install

git clone https://github.com/Lornatang/Real_ESRGAN-PyTorch.git
cd Real_ESRGAN-PyTorch
pip install -r requirements.txt
pip install -e . -v

All pretrained model weights

• realesrnet_x4
• realesrgan_x4
• discriminator_for_unet_x4

Inference

# Download pretrained model weights to `./results/pretrained_models`
wget https://github.com/Lornatang/Real_ESRGAN-PyTorch/releases/download/0.1.0/realesrgan_x4-df2k_degradation.pkl -O results/pretrained_models/realesrgan_x4-df2k_degradation.pkl
python demo/inference_images.py configs/inference/images.yaml
# You will see
# Model summary: Params: 16.70 M, GFLOPs: 73.43 B
# SR image save to `demo/output/00003.jpg`
# SR image save to `demo/output/0030.jpg`
# SR image save to `demo/output/0014.jpg`

Bicubic	Real_ESRGAN

Inference video

# Download pretrained model weights to `./results/pretrained_models`
wget https://github.com/Lornatang/Real_ESRGAN-PyTorch/releases/download/0.1.0/realesrgan_x4-df2k_degradation.pkl -O results/pretrained_models/realesrgan_x4-df2k_degradation.pkl
# Download test demo video to `./demo`
wget https://github.com/ckkelvinchan/RealBasicVSR/blob/master/data/demo_001.mp4 -O demo/demo_001.mp4
python demo/inference_video.py configs/inference/video.yaml
# You will see
# Model summary: Params: 16.70 M, GFLOPs: 73.43 B
# Processing: 
# SR image save to `demo/output/demo_001.avi`

Train

See training.md

Result

Source of original paper results: https://arxiv.org/pdf/2107.10833v2.pdf

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

Method	Scale	Set5 (NIQE)	Set14 (NIQE)
RealESRNet	4	-(9.80)	-(7.08)
RealESRGAN	4	-(7.09)	-(4.74)

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

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

Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan

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. at this https URL.

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

@InProceedings{wang2021realesrgan,
    author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
    title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
    booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
    date      = {2021}
}