# LLFlow
**Repository Path**: zzb32/LLFlow
## Basic Information
- **Project Name**: LLFlow
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-11-17
- **Last Updated**: 2024-11-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://paperswithcode.com/sota/low-light-image-enhancement-on-lol?p=low-light-image-enhancement-with-normalizing)
# [AAAI 2022 Oral] Low-Light Image Enhancement with Normalizing Flow
### [Paper](https://arxiv.org/pdf/2109.05923.pdf) | [Project Page](https://wyf0912.github.io/LLFlow/)
**Low-Light Image Enhancement with Normalizing Flow**
_Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-pui Chau, Alex C. Kot_
In AAAI'2022
## Overall
### Quantitative results
#### Evaluation on LOL
The evauluation results on LOL are as follows
| Method | PSNR | SSIM | LPIPS |
| :-- | :--: | :--: | :--: |
| LIME | 16.76 | 0.56 | 0.35 |
| RetinexNet | 16.77 | 0.56 | 0.47 |
| DRBN | 20.13 | 0.83 | 0.16 |
| Kind | 20.87 | 0.80 | 0.17 |
| KinD++ | 21.30 | 0.82 | 0.16 |
| **LLFlow (Ours)** | **25.19** | **0.93 (0.86)** | **0.11** |
(Our method measures SSIM value on grayscale images by strictly following SSIM's official code. We also assume that all LoL methods follow this [offical code](https://ece.uwaterloo.ca/~z70wang/research/ssim/). Since some recent Python implementations of SSIM also provide the option for non-grayscale images, the results obtained using such unofficial implementations have been provided within brackets.)
#### Computational Cost
The computational cost and performance of models are in the above table. We evaluate the cost using one image with a size 400×600. Ours(large) is the standard model reported in supplementary and Ours(small) is a model with reduced parameters. Both the training config files and pre-trained models are provided.
### Visual Results
## Get Started
### Dependencies and Installation
- Python 3.8
- Pytorch 1.9
1. Clone Repo
```
git clone https://github.com/wyf0912/LLFlow.git
```
2. Create Conda Environment
```
conda create --name LLFlow python=3.8
conda activate LLFlow
```
3. Install Dependencies
```
cd LLFlow
pip install -r ./code/requirements.txt
```
### Dataset
You can refer to the following links to download the datasets
[LOL](https://daooshee.github.io/BMVC2018website/), and
[LOL-v2](https://github.com/flyywh/CVPR-2020-Semi-Low-Light).
### Pretrained Model
We provide the pre-trained models with the following settings:
- A light weight model with promising performance trained on LOL [[Google drive](https://drive.google.com/file/d/1tukKu2KBZ_ohlQiLG4EKnrn1CAt_2F6G/view?usp=sharing)] with training config file `./confs/LOL_smallNet.yml`
- A standard-sized model trained on LOL [[Google drive](https://drive.google.com/file/d/1t3kASTRXbnEnCZ0EcIvGhMHYkoJ8E2C4/view?usp=sharing)] with training config file `./confs/LOL-pc.yml`.
- A standard-sized model trained on LOL-v2 [[Google drive](https://drive.google.com/file/d/1n7XwIlNr1lUxgZ9qlmFXCwzMTWSStQIW/view?usp=sharing)] with training config file `./confs/LOLv2-pc.yml`.
### Test
You can check the training log to obtain the performance of the model. You can also directly test the performance of the pre-trained model as follows
1. Modify the paths to dataset and pre-trained mode. You need to modify the following path in the config files in `./confs`
```python
#### Test Settings
dataroot_unpaired # needed for testing with unpaired data
dataroot_GT # needed for testing with paired data
dataroot_LR # needed for testing with paired data
model_path
```
2. Test the model
To test the model with paired data and obtain the evaluation results, e.g., PSNR, SSIM, and LPIPS. You need to specify the data path ```dataroot_LR```, ```dataroot_GT```, and model path ```model_path``` in the config file. Then run
```bash
python test.py --opt your_config_path
# You need to specify an appropriate config file since it stores the config of the model, e.g., the number of layers.
```
To test the model with unpaired data, you need to specify the unpaired data path ```dataroot_unpaired```, and model path ```model_path``` in the config file. Then run
```bash
python test_unpaired.py --opt your_config_path -n results_folder_name
# You need to specify an appropriate config file since it stores the config of the model, e.g., the number of layers.
```
You can check the output in `../results`.
### Train
All logging files in the training process, e.g., log message, checkpoints, and snapshots, will be saved to `./experiments`.
1. Modify the paths to dataset in the config yaml files. We provide the following training configs for both `LOL` and `LOL-v2` benchmarks. You can also create your own configs for your own dataset.
```bash
.\confs\LOL_smallNet.yml
.\confs\LOL-pc.yml
.\confs\LOLv2-pc.yml
```
You need to modify the following terms
```python
datasets.train.root
datasets.val.root
gpu_ids: [0] # Our model can be trained using a single GPU with memory>20GB. You can also train the model using multiple GPUs by adding more GPU ids in it.
```
2. Train the network.
```bash
python train.py --opt your_config_path
```
## Citation
If you find our work useful for your research, please cite our paper
```
@article{wang2021low,
title={Low-Light Image Enhancement with Normalizing Flow},
author={Wang, Yufei and Wan, Renjie and Yang, Wenhan and Li, Haoliang and Chau, Lap-Pui and Kot, Alex C},
journal={arXiv preprint arXiv:2109.05923},
year={2021}
}
```
## Contact
If you have any question, please feel free to contact us via yufei001@ntu.edu.sg.
(Please use your institution of shool email address to avoid being classfied as junk emails.)