https://github.com/RQ-Wu/UnderwaterRanker
[AAAI 2023 Oral] | Underwater Ranker: Learn Which Is Better and How to Be Better (underwater image enhancement + underwater image quality assessment)
https://github.com/RQ-Wu/UnderwaterRanker
pytorch underwater-image-enhancement
Last synced: 2 months ago
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[AAAI 2023 Oral] | Underwater Ranker: Learn Which Is Better and How to Be Better (underwater image enhancement + underwater image quality assessment)
- Host: GitHub
- URL: https://github.com/RQ-Wu/UnderwaterRanker
- Owner: RQ-Wu
- License: other
- Created: 2022-11-25T18:05:19.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-07-05T08:56:20.000Z (almost 2 years ago)
- Last Synced: 2023-08-09T04:59:35.134Z (almost 2 years ago)
- Topics: pytorch, underwater-image-enhancement
- Language: Python
- Homepage:
- Size: 8.34 MB
- Stars: 59
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Underwater Ranker: Learn Which Is Better and How to Be Better (AAAI 2023, Oral Presentation)
 
This repository contains the official implementation of the following paper:
> **Underwater Ranker: Learn Which Is Better and How to Be Better**
> Chunle Guo#, Ruiqi Wu#, Xin Jin, Linghao Han, Zhi Chai, Weidong Zhang, Chongyi Li*
> Proceedings of the AAAI conference on artificial intelligence (AAAI), 2023
[[Arxiv Paper](https://arxiv.org/abs/2208.06857)] [δΈζη (TBD)] [[Project](https://li-chongyi.github.io/URanker_files/)] [[Dataset (pwd: nuin)](https://pan.baidu.com/s/1K29p3gJWYa1ZM0vMHqI4uA)]
## News
- [07/05/2023] We released the Google Drive link of our model checkpoints.
- [03/30/2023] Our URanker has been added in Pytorch IQA.
- [01/11/2023] Our paper was awarded oral presentation qualification by AAAI.
## Dependencies and Installation
1. Clone Repo
```bash
git clone https://github.com/RQ-Wu/UnderwaterRanker.git
cd UnderwaterRanker
```
2. Create Conda Environment
```bash
conda env create -f environment.yaml
conda activate underwater_ranker
```
## Get Started
### Prepare pretrained models & dataset
1. You are supposed to download our pretrained model first in the links below and put them in dir `./checkpoints/`:
Model
SRCC/ KRCC (PSNR / SSIM)
:link: Download Links
URanker
0.8655 / 0.7402
[Google Drive]
[Baidu Disk (pwd: fu7y)]
NU2Net
22.669 / 0.9246
2. Two datasets used in our work can be downloaded in the links below:
- URankerSet: [Google Drive (TBD)] [Baidu Disk (pwd: nuin)]
- Underwater Image Enhancement Benchmark (UIEB): [Download Link]
The data is put in dir `./data/`.
**The directory structure will be arranged as**:
```
checkpoints
|- URanker_ckpt.pth
|- NU2Net_ckpt.pth
data
|- UIEB
|- raw-890
|- reference-890
|- UIERank
```
### Quick demo
Run demos to process the images in dir `./examples/` by following commands:
```bash
python ranker_demo.py \
--opt_path options/URanker.yaml \
--checkpoint_path checkpoints/URanker_ckpt.pth \
--input_path examples/ranker_example \
--save_path results/ranker_result.txt
```
```bash
python uie_demo.py \
--opt_path options/NU2Net.yaml \
--checkpoint_path checkpoints/NU2Net_ckpt.pth \
--input_path examples/uie_example \
--save_path results
```
### Training & Evaluation
Our training and evaluation configures are provided in `options/URanker.yaml` (for URanker) and `options/NU2Net.yaml` (for NU2Net)
Run the following commands for training:
```bash
python ranker_main_train.py --opt_path options/URanker.yaml
python uie_main_train.py --opt_path options.NU2Net.yaml
```
Run the following commands for evaluation:
```bash
python ranker_main_test.py --opt_path options/URanker.yaml --test_ckpt_path checkpoints/URanker_ckpt.pth
python uie_main_test.py --opt_path options.NU2Net.yaml --test_ckpt_path checkpoints/NU2Net_ckpt.pth --save_image
```
## Citation
If you find our repo useful for your research, please cite us:
```
@inproceedings{guo2023uranker,
title={Underwater Ranker: Learn Which Is Better and How to Be Better},
author={Guo, Chunle and Wu, Ruiqi and Jin, Xin and Han, Linghao and Chai, Zhi and Zhang, Weidong and Li, Chongyi},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2023}
}
```
## License
Licensed under a [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/) for Non-commercial use only.
Any commercial use should get formal permission first.
## Acknowledgement
This repository is maintained by [Ruiqi Wu](https://rq-wu.github.io/).