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
- To Note that: we only put a subset of REDS4 and Vid4 to save space, please download the full testset in official website, RED and Vid. More detail can refer to data preparation
- SC_burst (Smartphone burst) Dataset: we captured 16 burst images using smartphones, and put one burst of Scene 00 in sub_SC_burst. We unified raw format and saved SC_burst in ".MAT", where the raw data and metadata are stored.
- Whole dataset: BaiduYun with password d8u8.
- Bayer pattern: Our model is trained only use RGGB. Thus when testing raw images with other patterns (e.g., GRBG), don't forget to unified bayer pattern to RGGB by padding or flipping.
python /code/test_real.py
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training data preparation: Please refer to the "Video Super-Resolution" part of data preparation. To create LMDB dataset, please run create_lmdb.py.
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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
- Refer to the requirement.txt
- We utilize pytorch 1.2 and the deformable version does not support pytorch > 1.3. Thus when you use newest pytorch, please replace deformable version to newest (refer to BasicSR).
@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!