Implement of our GCP-Net.
Arxiv: https://arxiv.org/abs/2101.09870
IEEE Final Version: https://ieeexplore.ieee.org/document/9503334
store in gcpnet_model/600000_G.pth
set data_mode in test.py to 'REDS4' and 'Vid4', the default noise level is set as the 'high noise level' mentioned in the paper.
python /codes/test.py
python /code/test_real.py
training data preparation: Please refer to the "Video Super-Resolution" part of data preparation. To create LMDB dataset, please run create_lmdb.py.
change training options in train_GCP_Net.yml
python -m torch.distributed.launch --nproc_per_node=2 --master_port=4540 train.py -opt options/train/train_GCP_Net.yml --launcher pytorch
@article{guo2021joint,
title={Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images},
author={Guo, Shi and Liang, Zhetong and Zhang, Lei},
journal={arXiv preprint arXiv:2101.09870},
year={2021}
}
This repo is built upon the framework of EDVR, and we borrow some code from Unprocessing denoising, thanks for their excellent work!