Official PyTorch implementation of "Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask" Project | Paper

This codebase was developed and tested on Ubuntu with PyTorch 1.3.1 and CUDA 10.1, Python 3.7.10 and Scipy 1.1.0. To install PyTorch:

conda install pytorch==1.3.1 torchvision==0.4.2 cudatoolkit=10.1 -c pytorch

Set the dataset path and run:

python train.py --dataset_root path/to/dataset

Run the following commmand for help / more options like batch size, sequence length etc.

python train.py --h

To get visualization of the training, you can run tensorboard from the project directory using the command:

tensorboard --logdir logs --port 6007

and then go to https://localhost:6007.

@InProceedings{paliwal2021maskdenosing,
  author={Paliwal, Avinash and Zeng, Libing and Kalantari, Nima Khademi},
  booktitle={2021 IEEE International Conference on Computational Photography (ICCP)}, 
  title={Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask}, 
  year={2021},
  pages={1-10}
}

Parts of training code are adopted from SPADE, RAFT, UPI and RViDeNet.