本项目包含了以下论文的 MindSpore 实现：
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Ruiqi Wu, Zhengpeng Duan, Chunle Guo*, Zhi Chai, Chongyi Li （ * indicates corresponding author)
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
[Arxiv Paper] [中文版 (TBD)] [Website Page] [Dataset (pwd:qqqo)]
文章官方版本仓库链接: https://github.com/RQ-Wu/RIDCP_dehazing
目前已经完成大部分代码的 MindSpore 转化
- 完整代码的 MindSpore 实现
- Ubuntu >= 18.04
- CUDA >= 11.0
- MindSpore Nightly
- MindCV
- Other required packages in
requirements.txt
# git clone this repository
git clone https://github.com/RQ-Wu/RIDCP.git
cd RIDCP
# create new anaconda env
conda create -n ridcp python=3.8
conda activate ridcp
# install python dependencies
pip install -r requirements.txt
BASICSR_EXT=True python setup.py develop
- Downloading pretrained checkpoints
| Model | Description | 🔗 Download Links |
|---|---|---|
| HQPs | VQGAN pretrained on high-quality data. |
[Google Drive (TBD)] [Baidu Disk (TBD)] |
| RIDCP | Dehazing network trained on data generated by our pipeline. | |
| CHM | Weight for adjusting controllable HQPs matching. |
- Preparing data for training
| Dataset | Description | 🔗 Download Links |
|---|---|---|
| rgb_500 | 500 clear RGB images as the input of our phenomenological degradation pipeline | [Google Drive (TBD)] [Baidu Disk (pwd: qqqo)] |
| depth_500 | Corresponding depth map generated by RA-Depth(https://github.com/hmhemu/RA-Depth). | |
| Flick2K, DIV2K | High-quality data for VQGAN pre-training | - |
- The final directory structure will be arranged as:
datasets
|- clear_images_no_haze_no_dark_500
|- xxx.jpg
|- ...
|- depth_500
|- xxx.npy
|- ...
|- Flickr2K
|- DIV2K
pretrained_models
|- pretrained_HQPs.pth
|- pretrained_RIDCP.pth
|- weight_for_matching_dehazing_Flickr.pth
Run demos to process the images in dir ./examples/ by following commands:
python inference_ridcp.py -i examples -w pretrained_models/pretrained_RIDCP.pth -o results --use weight --alpha -21.25
Step 1: Pretrain a VQGAN on high-quality dataset
TBD
Step 2: Train our RIDCP
CUDA_VISIBLE_DEVICES=X,X,X,X python basicsr/train.py --opt options/RIDCP.yml
Step3: Adjust our RIDCP
TBD