PNNP (Evaluation Only)

The training code will be available after the paper is accepted.

Currently, this project is only used to evaluate the performance of denoising models trained based on PNNP.
We also provide a evaluation service for comparative methods, allowing everyone to verify various comparison methods under a unified low-light denoising dataset codebase.
Currently, supported comparison include:

1. Paired Data (baseline)
2. P-G
3. ELD
4. SFRN
5. NoiseFlow
6. PMN (MM/TPAMI)

We will provide support for additional comparative methods in the future.

📋 Prerequisites

• Python >=3.6, PyTorch >= 1.6
• Requirements: opencv-python, rawpy, exifread, h5py, scipy
• Platforms: Ubuntu 16.04, cuda-10.1
• Our method can run on the CPU, but we recommend you run it on the GPU

Please download the datasets first, which are necessary for evaluation (or training).
ELD (official project): download (11.46 GB)
SID (official project): download (25 GB)
LRID (official project): download (523 GB), including LRID_raw (523 GB, all data), LRID (185.1 GB, for training), results (19.92 GB, PMN visual results) and metrics (59KB, pkl files). Just download LRID (185.1 GB) is ok.

Before the manuscript is accepted, we only provide the weights and results of PNNP for denoising. You can download them at [Baidu Netdisk].
checkpoints should be downloaded into this project. The arrangement of resources can be found in ReadMe.txt under the folder. samples contains all the denoising results of PNNP.
If you choose to save them in a different directory, please remember to update the path location within the respective yaml files (runfiles/$camera_type$/$method$.yml).

🎬 Quick Start

1. use get_dataset_infos.py to generate dataset infos (please modify --root_dir)

# Evaluate
python3 get_dataset_infos.py --dstname ELD --root_dir /data/ELD --mode SonyA7S2
python3 get_dataset_infos.py --dstname SID --root_dir /data/SID/Sony --mode evaltest
python3 get_dataset_infos.py --dstname LRID --root_dir /data/LRID
# Train
python3 get_dataset_infos.py --dstname SID --root_dir /data/SID/Sony --mode train
# python3 get_dataset_infos.py --dstname LRID --root_dir /data/LRID

2. evaluation

Obviously, you can replace PNNP with other method names. We now provide the weights of P-G, ELD, SFRN, NoiseFlow, LRD, and PMN.

# ELD & SID
python3 trainer_SID.py -f runfiles/SonyA7S2/PNNP.yml --mode evaltest
# ELD only
python3 trainer_SID.py -f runfiles/SonyA7S2/PNNP.yml --mode eval
# SID only
python3 trainer_SID.py -f runfiles/SonyA7S2/PNNP.yml --mode test
# LRID
python3 trainer_LRID.py -f runfiles/IMX686/PNNP.yml --mode evaltest

If you don't want to save pictures, please add --save_plot False. This option will save your time and space.

3. training (not provided yet)

# SID (SonyA7S2)
python3 trainer_PNNP_SID.py -f runfiles/SonyA7S2/Ours.yml --mode train
# LRID (IMX686)
python3 trainer_PNNP_LRID.py -f runfiles/IMX686/Ours.yml --mode train

🏷️ Citation

Please cite our paper if you find our code helpful in your research or work.

@article{feng2023physics,
  title={Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising},
  author={Feng, Hansen and Wang, Lizhi and Huang, Yiqi and Wang, Yuzhi and Zhu, Lin and Huang, Hua},
  journal={arXiv preprint arXiv:2310.09126},
  year={2023}
}

📧 Contact

If you would like to get in-depth help from me, please feel free to contact me (fenghansen@bit.edu.cn / hansen97@outlook.com) with a brief self-introduction (including your name, affiliation, and position).