https://github.com/kozistr/esrgan-tensorflow
Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution
https://github.com/kozistr/esrgan-tensorflow
esrgan resolution tensorflow
Last synced: 22 days ago
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Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution
- Host: GitHub
- URL: https://github.com/kozistr/esrgan-tensorflow
- Owner: kozistr
- License: apache-2.0
- Created: 2019-03-21T04:43:22.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-06-02T13:39:47.000Z (over 6 years ago)
- Last Synced: 2025-09-02T13:55:41.021Z (about 1 month ago)
- Topics: esrgan, resolution, tensorflow
- Language: Python
- Homepage: https://arxiv.org/abs/1809.00219
- Size: 56.6 KB
- Stars: 34
- Watchers: 5
- Forks: 5
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ESRGAN in tensorflow
Enhanced Super Resolution Generative Adversarial Network in tensorflow
This repo is based on pytorch impl [original here](https://github.com/xinntao/ESRGAN)
**Work In Process :)**
[](https://lgtm.com/projects/g/kozistr/ESRGAN-tensorflow/alerts/)
[](https://lgtm.com/projects/g/kozistr/ESRGAN-tensorflow/context:python)
# Requirements
* python 2.x / 3.x
* tensorflow-gpu 1.x
* opencv
* glob
* tqdm
# Repo-Tree
```
│
├── output (generated images)
│ ├── ...
│ └── xxx.png
├── tb_logs (tensorboard records)
│ ├── [unique id]
│ │ ├── *.ckpt
│ │ ├── *.tsv
│ │ ├── *.meta
│ │ └── ...
│ └── [unique id]
├── requirements.txt (requirements)
├── readme.md (explaination)
├── losses.py (useful losses)
├── metrics.py (useful metrics)
├── model.py (ESRGAN model)
├── main.py (trainer / inferener)
├── config.py (global configurations)
├── tfutils.py (useful TF utils)
├── utils.py (image processing utils)
└── dataloader.py (DataSet loader)
```
# Usage
1. Clone this github repo.
```
git clone https://github.com/kozistr/ESRGAN-tensorflow
cd ESRGAN-tensorflow
```
2. install required packages (if needed)
```
# with pip
python -m pip install -r requirements.txt
# with conda
conda install --yes --file requirements.txt
```
3. run scripts!
For training,
```python3 train.py```
For evaluation,
```python3 evaluate.py```
For inference,
```python3 inference.py --src test-lr.png --dst test-hr.png```
# Results
# Citation
```
@InProceedings{wang2018esrgan,
author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
title = {ESRGAN: Enhanced super-resolution generative adversarial networks},
booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},
month = {September},
year = {2018}
}
```
# Author
HyeongChan kim / [kozistr](http://kozistr.tech)