Practical Mobile Raw Image Denoising (PMRID)

Code and dataset for ECCV20 paper Practical Deep Raw Image Denoising on Mobile Devices.

Dataset

Downloads

• OneDrive
• Kaggle

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:

{
   "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 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

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}
}