RAW-to-sRGB
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)
PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)
PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
1. Framework
Figure 1: Illustration of the proposed joint learning framework.
2. Results
Figure 2: Example of data pairs of ZRR and SR-RAW datasets, where clear spatial misalignment can be observed with the reference line. With such inaccurately aligned training data, PyNet [22] and Zhang et al. [62] are prone to generating blurry results with spatial misalignment, while our results are well aligned with the input.
3. Preparation
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3.1 Prerequisites
- PyTorch (v1.6)
- Python 3.x, with OpenCV, Numpy, CuPy, Pillow and tqdm, and tensorboardX is used for visualization
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3.2 Dataset
- Zurich RAW to RGB dataset and SR-RAW Dataset are used for experiments.
4. Quick Start
We show some exemplar commands here for better introduction.
4.1 Training
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Zurich RAW to RGB dataset
python train.py \ --dataset_name eth --model zrrjoint --name $name --gcm_coord True \ --ispnet_coord True --niter 80 --lr_decay_iters 40 --save_imgs False \ --batch_size 16 --print_freq 300 --calc_psnr True --lr 1e-4 -j 8 \ --dataroot /data/dataset/Zurich-RAW-to-DSLR -
SR-RAW Dataset
To be continued...
4.2 Testing
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The pre-trained models will be released soon.
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Zurich RAW to RGB dataset
python test.py \ --model zrrjoint --name zrrjoint --dataset_name eth --ispnet_coord True --alignnet_coord True \ --load_iter 80 --save_imgs True --calc_psnr True --gpu_id 0 --visual_full_imgs False \ --dataroot /data/dataset/Zurich-RAW-to-DSLR -
SR-RAW Dataset
To be continued...
4.3 Note
- You can specify which GPU to use by
--gpu_ids, e.g.,--gpu_ids 0,1,--gpu_ids 3,--gpu_ids -1(for CPU mode). In the default setting, all GPUs are used. - You can refer to options for more arguments.
5. Citation
If you find it useful in your research, please consider citing:
@inproceedings{RAW-to-sRGB,
title={Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision},
author={Zhang, Zhilu and Wang, Haolin and Liu, Ming and Wang, Ruohao and Zuo, Wangmeng and Zhang, Jiawei},
booktitle={ICCV},
year={2021}
}
6. Acknowledgement
This repo is built upon the framework of CycleGAN, and we borrow some code from PyNet, Zoom-Learn-Zoom, PWC-Net and AdaDSR, thanks for their excellent work!
