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

• 3.1 Prerequisites

  • PyTorch (v1.6)
  • Python 3.x, with OpenCV, Numpy, CuPy, Pillow and tqdm, and tensorboardX is used for visualization

• 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

• 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

• The pre-trained models will be released soon.

• 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!