Paper | Project Page | Video
Shangchen Zhou, Chongyi Li, Chen Change Loy
S-Lab, Nanyang Technological University
- 2022.09.27: The code and pre-trained models are released. 🤗
- 2022.07.04: This repo is created.
- Built and train a new network for low-light enhancement
- Add checkpoint of LEDNetGAN (trained with GAN)
- Add checkpoint of retrained LEDNet (retrained on BasicSR)
- Add checkpoint of LEDNet in paper
(All data is hosted on Google Drive)
| Dataset | Link | Number | Description |
|---|---|---|---|
| LOL-Blur | Google Drive | 12,000 | A total of 170 videos for training and 30 videos for testing, each of which has 60 frames, amounting to 12,000 paired data. (Note that the first and last 30 frames of each video are NOT consecutive, and their darknesses are simulated differently as well.) |
| Real-LOL-Blur | Google Drive | 1354 | 482 real-world night blurry images (from RealBlur-J Dataset) + 872 real-world night blurry images acquired by Sony RX10 IV camera. |
[Unfold] for detailed description of each folder in LOL-Blur dataset:
|
[1] This method use distorted image as reference. Please refer to the paper for details.
[2] Currently, only naive random forest regression is implemented and does not support backward.
- Pytorch >= 1.7.1
- CUDA >= 10.1
- Other required packages in
requirements.txt
# git
8960
clone this repository
git clone https://github.com/sczhou/LEDNet
cd LEDNet
# create new anaconda env
conda create -n lednet python=3.8 -y
conda activate lednet
# install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop
Before training, you need to:
- Download the LOL-Blur Dataset from [Google Drive].
- Specify
dataroot_gtanddataroot_lqin the corresponding option file.
Training LEDNet:
# without GAN
python basicsr/train.py -opt options/train_LEDNet.yml
# with GAN
python basicsr/train.py -opt options/train_LEDNetGAN.yml
This project is built on BasicSR, the detailed tutorial on training commands and config settings can be found here.
-
Download the LEDNet pretrained model from [Release V0.1.0] to the
weightsfolder. You can manually download the pretrained models OR download by runing the following command.python scripts/download_pretrained_models.py LEDNet
Inference LEDNet:
# test LEDNet (paper model)
python inference_lednet.py --model lednet --test_path ./inputs
# test retrained LEDNet (higher PSNR and SSIM)
python inference_lednet.py --model lednet_retrain --test_path ./inputs
# test LEDNetGAN
python inference_lednet.py --model lednetgan --test_path ./inputs
The results will be saved in the results folder.
# set evaluation metrics of 'psnr', 'ssim', and 'lpips (vgg)'
python scripts/calculate_iqa_pair.py --result_path 'RESULT_ROOT' --gt_path 'GT_ROOT' --metrics psnr ssim lpips
(The released model was retrained using the BasicSR framework, which makes it easier to use or further develop upon this work. NOTE that the PSNR and SSIM scores of retrained model are higher than the paper model.)
-
Download the CE-ZeroDCE pretrained model from [Release V0.1.0] to the
weightsfolder. You can manually download the pretrained models OR download by runing the following command.python scripts/download_pretrained_models.py CE-ZeroDCE
Run low-light generation:
python scripts/generate_low_light_imgs.py --test_path 'IMG_ROOT' --result_path 'RESULT_ROOT' --model_path './weights/ce_zerodce.pth'
This project is licensed under S-Lab License 1.0. Redistribution and use for non-commercial purposes should follow this license.
This project is based on BasicSR. We calculate evaluation metrics using IQA-PyTorch toolbox. Thanks for their awesome works.
If our work is useful for your research, please consider citing:
@InProceedings{zhou2022lednet,
author = {Zhou, Shangchen and Li, Chongyi and Loy, Chen Change},
title = {LEDNet: Joint Low-light Enhancement and Deblurring in the Dark},
booktitle = {ECCV},
year = {2022}
}If you have any questions, please feel free to reach me out at shangchenzhou@gmail.com.