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ICASSP 2020 - ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network - ICPR 2020 - Tarsier: Evolving Noise Injection in Super-Resolution GANs

License: Apache License 2.0

Python 94.06% MATLAB 5.94%

esrganplus's Introduction

ESRGAN+ nESRGAN+ Tarsier

ICASSP 2020 - ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network

Paper arXiv

Paper IEEE Xplore

ICPR 2020 - Tarsier: Evolving Noise Injection in Super-Resolution GANs

Paper arXiv

Dependencies

Python 3 (Recommend to use Anaconda)
PyTorch >= 1.0.0
NVIDIA GPU + CUDA
Python packages: pip install numpy opencv-python lmdb tensorboardX

How to test

Place your low-resolution images in test_image/LR folder.
Download pretrained models from Google Drive and place them in test_image/pretrained_models.
Run the command: python test_image/test.py test_image/pretrained_models/nESRGANplus.pth (or any other models).
The results are in test_image/results folder.

How to train

Prepare the datasets which can be downloaded from Google Drive.
Prepare the PSNR-oriented pretrained model (all pretrained models can be downloaded from Google Drive).
Modify the configuration file codes/options/train/train_ESRGANplus.json.
Run the command python train.py -opt codes/options/train/train_ESRGANplus.json.

Acknowledgement

This code is based on BasicSR.

Citation

@INPROCEEDINGS{9054071,
    author = {N. C. {Rakotonirina} and A. {Rasoanaivo}},  
    booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},   
    title={ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network},   
    year={2020},  
    volume={},  
    number={},  
    pages={3637-3641},}

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