The implementation of CVPR 2020 (Oral) paper "A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising" and its journal (TPAMI) version "Physics-based Noise Modeling for Extreme Low-light Photography". Interested readers are also referred to an insightful Note about this work in Zhihu (Chinese).

• 2022/01/08: Major Update: Release the training code and other related items (including synthetic datasets, customized rawpy, calibrated camera noise parameters, baseline noise models, calibrated SonyA7S2 camera response function (CRF) and a modern implementation of EMoR radiometric calibration method) to accelerate further research!
• 2022/01/05: Replace the released ELD dataset by my local version of the dataset. We thank @fenghansen for pointing this out. Please refer to this issue for more details.
• 2021/08/05: The comprehensive version of this work was accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• 2020/07/16: Release the ELD dataset and our pretrained models at GoogleDrive and Baidudisk (0lby)

• We present a highly accurate noise formation model based on the characteristics of CMOS photosensors, thereby enabling us to synthesize realistic samples that better match the physics of image formation process.

• To study the generalizability of a neural network trained with existing schemes, we introduce a new Extreme Low-light Denoising (ELD) dataset that covers four representative modern camera devices for evaluation purposes only. The image capture setup and example images are shown as below:

• By training only with our synthetic data, we demonstrate a convolutional neural network can compete with or sometimes even outperform the network trained with paired real data under extreme low-light settings. The denoising results of networks trained with multiple schemes, i.e. 1) synthetic data generated by the poissonian-gaussian noise model, 2) paired read data of SID dataset and 3) synthetic data generated by our proposed noise model, are displayed as follows:

• Python >=3.6, PyTorch >= 1.6
• Requirements: opencv-python, tensorboardX, lmdb, rawpy, torchinterp1d
• Platforms: Ubuntu 16.04, cuda-10.1

Notice this codebase relies on my own customized rawpy, which provides more functionalities than the official one. This is released together with our datasets and the pretrained models (See GoogleDrive or Baidudisk (0lby)) To build rawpy from source, please first compile and install the LibRaw library following the official instructions, then type pip install -e . in the customized rawpy directory.

Due to the business license, we are unable to to provide the noise model as well as the calibration method. Instead, we release our collected ELD dataset and our pretrained models to facilitate future research.

To reproduce our results presented in the paper (Table 1 and 2), please take a look at scripts/test_SID.sh and scripts/test_ELD.sh

Update: (2022-01-08) We release the training code and the synthetic datasets per the users' requests. The training scripts and the user instructions can be found in scripts/train.sh. Additionally, we provide the baseline noise models (G/G+P/G+P*) and the calibrated noise parameters for all cameras of ELD for training (see noise.py and train_syn.py), which could serve as a starting point to develop your own noise model.

We use lmdb to prepare datasets, please refer to util/lmdb_data.py to see how we generate datasets from SID. We also provide a new implementation of a classic radiometric calibration method EMoR, and utilize it to calibrate the CRF of SonyA7S2, which could be further used to simulate realistic on-board ISP as in the commercial SonyA7S2 camera.

The dataset capture protocol is shown as follow:

We choose three ISO settings (800, 1600, 3200) and four low light factors (x1, x10, x100, x200) to capture the dataset (x1/x10 is not used in our paper). Image ids 1, 6, 11, 16 represent the long-exposure reference images. Please refer to ELDEvalDataset class in data/sid_dataset.py for more details.

If you find our code helpful in your research or work please cite our paper.

@article{wei2021physics,
  title={Physics-based noise modeling for extreme low-light photography},
  author={Wei, Kaixuan and Fu, Ying and Zheng, Yinqiang and Yang, Jiaolong},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={11},
  pages={8520--8537},
  year={2021},
  publisher={IEEE}
}

@inproceedings{wei2020physics,
  title={A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising},
  author={Wei, Kaixuan and Fu, Ying and Yang, Jiaolong and Huang, Hua},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020},
}

If you find any problem, please feel free to contact me (kxwei at princeton.edu kaixuan_wei at bit.edu.cn). A brief self-introduction (including your name, affiliation and position) is required, if you would like to get an in-depth help from me. I'd be glad to talk with you if more information (e.g. your personal website link) is attached. Note I would not reply to any impolite/aggressive email that violates the above criteria.