This repository contains official implementation of Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes in CVPR 2020, by Huanjing Yue, Cong Cao, Lei Liao, Ronghe Chu, and Jingyu Yang.
http://openaccess.thecvf.com/content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf
http://openaccess.thecvf.com/content_CVPR_2020/supplemental/Yue_Supervised_Raw_Video_CVPR_2020_supplemental.pdf
You can download our dataset from Google Drive or MEGA or Baidu Netdisk (key: cdux). We also provide original averaged frame (without applying BM3D) in folder "indoor_raw_noisy", named like "frameXX_clean.tiff". The Bayer pattern of raw data is GBRG, the black level is 240, the white level is 2^12-1. You can apply your ISP to raw data to generate sRGB video denoising data.
The CRVD dataset is available for the academic purpose only. Any researcher who uses the CRVD dataset should obey the licence as below:
All of the CRVD Dataset (data and software) are copyright by Intelligent Imaging and Reconstruction Laboratory, Tianjin University and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.
This dataset is for non-commercial use only. However, if you find yourself or your personal belongings in the data, please contact us, and we will immediately remove the respective images from our servers.
cd ./modules/DCNv2
bash make.sh
cd ./modules/cc_attention
python setup.py develop
python sRGB_to_raw.py
python synthesize_noise.py
python test_indoor.py --model pretrain --gpu_id 0 --output_dir ./results/pretrain/ --vis_data True
python test_indoor.py --model finetune --gpu_id 0 --output_dir ./results/finetune/ --vis_data True
python train_isp.py --gpu_id 0 --num_epochs 770 --patch_size 512
python train_predenoising.py --gpu_id 0 --num_epochs 700 --patch_size 128
python train_pretrain.py --gpu_id 0 --num_epochs 33 --patch_size 128 --batch_size 1
python train_finetune.py --gpu_id 0 --num_epochs 70 --patch_size 128 --batch_size 1
If you find our dataset or code helpful in your research or work, please cite our paper:
@inproceedings{yue2020supervised,
title={Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes},
author={Yue, Huanjing and Cao, Cong and Liao, Lei and Chu, Ronghe and Yang, Jingyu},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2020}
}
Our work and implementations are inspired by following projects:
[Unprocessing] (https://github.com/google-research/google-research/tree/master/unprocessing)
[EDVR] (https://github.com/xinntao/EDVR)
[SID] (https://github.com/cchen156/Learning-to-See-in-the-Dark)
[DANet] (https://github.com/junfu1115/DANet)
[CCNet] (https://github.com/speedinghzl/CCNet)