# SIED **Repository Path**: niyayu/SIED ## Basic Information - **Project Name**: SIED - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-07-02 - **Last Updated**: 2025-07-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # [ICCV 2025] Learning to See in the Extremely Dark [[Paper]](https://arxiv.org/pdf/2506.21132)

Hai Jiang1, Binhao Guan2, Zhen Liu2, Xiaohong Liu3, Jian Yu4, Zheng Liu4, Songchen Han1, Shuaicheng Liu2

1.Sichuan University,

2.University of Electronic Science and Technology of China,

3.Shanghai Jiaotong University,

4.National Innovation Center for UHD Video Technology ## Dataset synthesis pipeline ![](./Figure/syn_pipe.png) ## Framework pipeline ![](./Figure/framework.png) ## Dependencies ``` pip install -r requirements.txt ```` ## Download the raw training and evaluation datasets ### SIED dataset Coming soon! ### SID dataset ## Pre-trained Models You can download our pre-trained model from [[Google Drive]]() and [[Baidu Yun (extracted code:)]]() ## How to train? You need to modify ```datasets/dataset.py``` slightly for your environment, and then ``` python train.py ``` ## How to test? ``` python evaluate.py ``` ## Visual comparison ![](./Figure/visual_canon.png) ![](./Figure/visual_sony.png) ## Citation If you use this code or ideas from the paper for your research, please cite our paper: ``` ``` ## Acknowledgement Part of the code is adapted from previous works: [WeatherDiff](https://github.com/IGITUGraz/WeatherDiffusion) and [MIMO-UNet](https://github.com/chosj95/MIMO-UNet). We thank all the authors for their contributions.