https://github.com/megengine/pmrid
ECCV2020 - Practical Deep Raw Image Denoising on Mobile Devices
https://github.com/megengine/pmrid
denoise megengine
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ECCV2020 - Practical Deep Raw Image Denoising on Mobile Devices
- Host: GitHub
- URL: https://github.com/megengine/pmrid
- Owner: MegEngine
- License: apache-2.0
- Created: 2020-11-18T14:12:01.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-18T10:22:47.000Z (about 2 years ago)
- Last Synced: 2025-03-27T00:12:00.215Z (about 2 months ago)
- Topics: denoise, megengine
- Language: Python
- Homepage:
- Size: 7.18 MB
- Stars: 237
- Watchers: 8
- Forks: 39
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Practical Mobile Raw Image Denoising (PMRID)
Code and dataset for ECCV20 paper [Practical Deep Raw Image Denoising on Mobile Devices](https://arxiv.org/abs/2010.06935).
## Dataset
### Downloads
- [OneDrive](https://megvii-my.sharepoint.cn/:f:/g/personal/wangyuzhi_megvii_com/Et4v2Z7CkRxHnbcFUq6RXZMBfXUrlm_Se5OVDvcdujVsMA?e=vcfJWs)
- [Kaggle](https://www.kaggle.com/dataset/1bdc5cd707cfbb3ee842eb3cbfe93495dbba88017d29f295f8edbcb8f8790556)
### Usage
The dataset includes two 7zip files:
- `reno10x_noise.7z` contains DNG raw images shot by an _OPPO Reno 10x_ phone for noise parameter estimation (refer Sec 3.1 and 5.1 in the paper)
- `PMRID.7z` is the benchmark dataset described in Sec 5.2 in the paper
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`:
```python
{
"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]
]
}
}
```
## Pre-trained Models and Benchmark Script
Both [PyTorch](https://pytorch.org/) and [MegEngine](https://megengine.org.cn/) 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
```
## Citation
```
@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}
}
```