This is an official implementation of the paper "Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network". [PDF]
- Python 3.6
- PyTorch >= 1.1.0
- numpy
- cv2
- skimage
- DCNv2
- easydict
- yaml
Clone this github repo.
git clone https://github.com/Marinyyt/PVDD.git
cd PVDD
- Download PVDD|CRVD|DAVIS dataset and unpack them to any place you want.
- Run
train.py
using the corresponding yaml files. (Please change thedata_path
argument in yaml files and noise-level file path in Dataset class.)
# PVDD sRGB
python train.py --config /USER_PATH/PVDD/configs/PVDD_pvdd0815_02_charbo_bs1_pvdd_model.yaml --save_path /USER_SAVE_PATH
python train.py --config /USER_PATH/PVDD/configs/PVDD_pvdd0815_02_level_charbo_bs1_pvdd_model.yaml --save_path /USER_SAVE_PATH
# PVDD RAW
python train.py --config /USER_PATH/PVDD/configs/PVDD_pvdd0815_02_charbo_bs1_pvdd_raw_model.yaml --save_path /USER_SAVE_PATH
python train.py --config /USER_PATH/PVDD/configs/PVDD_pvdd0815_02_level_charbo_bs1_pvdd_raw_model.yaml --save_path /USER_SAVE_PATH
# CRVD sRGB
python train.py --config /USER_PATH/PVDD/configs/PVDD_pvdd0815_charbo_bs1_crvd_model.yaml --save_path /USER_SAVE_PATH
# DAVIS sRGB
python train.py --config /USER_PATH/PVDD/configs/PVDD_pvdd0815_charbo_bs1_davis_model.yaml --save_path /USER_SAVE_PATH
- You can find the results and logs in
save_path
.
- Download our pre-trained models and unpack them to any place you want or use your pre-trained models.
- Run.
# PVDD
python test_video_pvdd_server.py --model_file /USER_MODEL_CKPT_PATH --save_path /USER_SAVE_PATH --test_path /USER_TEST_DATA_PATH --num_frame 5
python test_video_pvdd_level_server.py --model_file /USER_MODEL_CKPT_PATH --save_path /USER_SAVE_PATH --test_path /USER_TEST_DATA_PATH --num_frame 5
python test_video_pvdd_raw_server.py --model_file /USER_MODEL_CKPT_PATH --save_path /USER_SAVE_PATH --test_path /USER_TEST_DATA_PATH --num_frame 5
python test_video_pvdd_level_raw_server.py --model_file /USER_MODEL_CKPT_PATH --save_path /USER_SAVE_PATH --test_path /USER_TEST_DATA_PATH --num_frame 5
# DAVIS
python test_video_davis_server.py --model_file /USER_MODEL_CKPT_PATH --save_path /USER_SAVE_PATH --test_path /USER_TEST_DATA_PATH --num_frame 5
# CRVD
python test_video_crvd_server.py --model_file /USER_MODEL_CKPT_PATH --save_path /USER_SAVE_PATH --test_path /USER_TEST_DATA_PATH --num_frame 5
Please download PVDD from Google Drive or Baidu Drive.
sRGB | raw | |
---|---|---|
Training Dataset | Google Drive, Baidu Drive | Google Drive, Baidu Drive |
Testing Dataset | Google Drive, Baiidu Drive | Google Drive, Baidu Drive |
If you find our work useful in your research or publication, please cite:
@article{xu2022pvdd,
title={Pvdd: A practical video denoising dataset with real-world dynamic scenes},
author={Xu, Xiaogang and Yu, Yitong and Jiang, Nianjuan and Lu, Jiangbo and Yu, Bei and Jia, Jiaya},
journal={arXiv preprint arXiv:2207.01356},
year={2022}
}