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.