Awesome
Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement (ECCV 2024)
Kun Zhou, Xinyu Lin, Wenbo Li, Xiaogang Xu, Yuanhao Cai, Zhonghang Liu, Xiaoguang Han, Jiangbo Lu
<!-- #### [\[Paper\]](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_NeRFLix_High-Quality_Neural_View_Synthesis_by_Learning_a_Degradation-Driven_Inter-Viewpoint_CVPR_2023_paper.pdf) [\[Project\]](https://t.co/uNiTd9ujCv) --- -->News, We have release the training and inference scripts for LoLv2 and our paper is available on [ARXIV].
<!-- This is the official implementation of AFD-LLIE. Our paper will soon be available on ARXIV. Our code will be made available before ECCV24. -->Visual results, along with uploaded checkpoints, can be accessed at [google drive].
<img src="assets/imgsli_2.png" height="300px"/>
<img src="assets/imgsli_3.png" height="300px"/>
<img src="assets/imgsli_4.png" height="300px"/>
We have explored ten SOTA LLIE baselines:
CNN: UNet (written by Chatgpt), [MIR-Net ECCV 2020], [MIR-Net-V2 T-PAMI 2022]
Transformer: [SNR CVPR 2022], [Retinexformer ICCV 2023 ], [Restormer CVPR 2020]
Mamba: [RetinexMamba], [MambaIR ECCV 2024],
Diffusion-based: [Diff-L Siggraph 2023] and Flow-based: [LLFlow AAAI 2022]).
Our proposed AFD-LLIE consistently and significantly enhances their performance.
Let's make UNet (by ChatGPT) great again!
Even a basic UNet can achieve impressive results and outperform Retinexformer with 24.25dB on LOL-v2.
<img src="assets/imgsli_1.png" height="223px"/>
Citation
If our work is useful for your research, please consider citing:
@inproceedings{zhou2024,
title={Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement},
author={Kun Zhou, Xinyu Lin, Wenbo Li, Xiaogang Xu, Yuanhao Cai, Zhonghang Liu, Xiaoguang Han, and Jiangbo Lu},
booktitle={Proceedings of the European Conference on Computer Vision},
year={2024}
}