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RAW-to-sRGB (ICCV 2021)

PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision

1. Framework

<img src="./figs/framework.png" width="95%"> Figure 1: Illustration of the proposed joint learning framework.

2. Results

<img src="./figs/results.png" width="95%"> 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

Prerequisites
- Python 3.x and PyTorch 1.6.
- OpenCV, NumPy, Pillow, CuPy, colour_demosaicing, tqdm, lpips, scikit-image and tensorboardX.
Dataset
- Zurich RAW to RGB dataset. It can also be downloaded from Baidu Netdisk.
- Preprocessed SR-RAW Dataset. Note that here we preprocessed the original SR-RAW dataset according to the code. You can also download the original SR-RAW dataset here.

4. Quick Start

4.1 Pre-trained models

The pre-trained models can be downloaded. You need to put them in the RAW-to-sRGB/ckpt/ folder.

4.2 Training

Zurich RAW to RGB dataset

sh train_zrr.sh
SR-RAW Dataset

sh train_srraw.sh

4.3 Testing

Zurich RAW to RGB dataset

sh test_zrr.sh
SR-RAW Dataset

sh test_srraw.sh

4.4 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!