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Code and dataset for ECCV20 paper Practical Deep Raw Image Denoising on Mobile Devices.
The dataset includes two 7zip files:
The structure of PMRID.7z is
- benchmark.json # meta info
- Scene1/
\- Bright/
\- exposure-case1/
\- input.raw # RAW data for noisy image in uint16
- gt.raw # RAW data for clean image in uint16
+ case2/
+ Dark/
+ Secne2/
All metadata for images are listed in benchmark.json:
{
"input": "path/to/noisy_input.raw",
"gt": "path/to/clean_gt.raw",
"meta": {
"name": "case_name",
"scene_id": "scene_name",
"light": "light condition",
"ISO": "ISO",
"exp_time": "exposure time",
"bayer_pattern": "BGGR",
"shape": [3000, 4000],
"wb_gain": [r_gain, g_gain, b_gain],
"CCM": [ # 3x3 color correction matrix
[c11, c12, c13],
[c21, c22, c23],
[c31, c32, c33]
],
"ROIs": [ # patch ROIs to calculate PSNR and SSIM, x0 is topleft
[topleft_w, topleft_h, bottomright_w, bottomright_h]
]
}
}Both PyTorch and MegEngine pre-trained models are provided in the models directory. The benchmark script is written for models trained with MegEngine. Python >= 3.6 is required to run the benchmark script.
pip install -r requirements.txt python3 run_benchmark.py --benchmark /path/to/PMRID/benchmark.json models/mge_pretrained.ckp
@inproceedings{wang2020,
title={Practical Deep Raw Image Denoising on Mobile Devices},
author={Wang, Yuzhi and Huang, Haibin and Xu, Qin and Liu, Jiaming and Liu, Yiqun and Wang, Jue},
booktitle={European Conference on Computer Vision (ECCV)},
year={2020},
pages={1--16}
}
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