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Low-Light Image Enhancement with Multi-stage Residue Quantization and Brightness-aware Attention (ICCV2023)
This repository contains the Pytorch codes for paper Low-Light Image Enhancement with Multi-stage Residue Quantization and Brightness-aware Attention (ICCV (2023)). [paper]
Overview
In this paper, we propose a brightness-aware network with normal-light priors based on brightness-aware attention and residualquantized codebook. To achieve a more natural and realistic enhancement, we design a query module to obtain more reliable normal-light features and fuse them with lowlight features by a fusion branch. In addition, we propose a brightness-aware attention module to further improve the robustness of the network to the brightness. Extensive experimental results on both real-captured and synthetic data show that our method outperforms existing state-of-the-art methods.
Architecture
Figure 2: Architectures of the proposed three-stage framework for low-light image enhancement.
Results
Results on LOLv1, LOLv2-real and LOLv2-synthetic dataset can be downloaded from [Google Drive]
Usage
Download the RQ-LLIE repository
- Requirements are Python 3 and PyTorch 1.8.0.
- Download this repository via git
git clone https://github.com/LiuYunlong99/RQ-LLIE
or download the zip file manually.
Prepare the Dataset
Download the following datasets:
LOLv1: [Google Drive]
LOLv2: [Google Drive]
Evaluation
Download the pretrained models from [Google Drive]. And put them in the folder ./pretrained_models .
# LOLv1
python test_LOLv1_v2_real.py -opt options/test/LOLv1.yml
# LOLv2-Real
python test_LOLv1_v2_real.py -opt options/test/LOLv2_real.yml
# LOLv2-Synthetic
python test_LOLv2_synthetic.py -opt options/test/LOLv2_synthetic.yml
Note you need to change the dataroot_GT and dataroot_LQ to your path in the option file.
Training
Train the model on the corresponding dataset using the train config. For example, the training on LOLv1:
python -m torch.distributed.launch --nproc_per_node 1 --master_port 4320 train.py -opt options/train/LOLv1.yml --launcher pytorch
Acknowledgements
This source code is inspired by SNR(CVPR22).
Citation Information
If you find our work useful for your research, please consider giving this project a star and citing the following papers :)
@InProceedings{Liu_2023_ICCV,
author = {Liu, Yunlong and Huang, Tao and Dong, Weisheng and Wu, Fangfang and Li, Xin and Shi, Guangming},
title = {Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware Attention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {12140-12149}
}