Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training (NeurIPS 2021)

News

• 2022.04.17 : Testing codes, pre-trained denoising models, and results have been released. :rocket:
• 2021.09.30 : Our paper has been accepted by NeurIPS 2021. 🔥

Gaussian Noise	PNGAN Noise

Abstract: Existing deep learning real denoising methods require a large amount of noisy-clean image pairs for supervision. Nonetheless, capturing a real noisy-clean dataset is an unacceptable expensive and cumbersome procedure. To alleviate this problem, this work investigates how to generate realistic noisy images. Firstly, we formulate a simple yet reasonable noise model that treats each real noisy pixel as a random variable. This model splits the noisy image generation problem into two sub-problems: image domain alignment and noise domain alignment. Subsequently, we propose a novel framework, namely Pixel-level Noise-aware Generative Adversarial Network (PNGAN). PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space to perform image domain alignment. Simultaneously, PNGAN establishes a pixel-level adversarial training to conduct noise domain alignment. Additionally, for better noise fitting, we present an efficient architecture Simple Multi-scale Network (SMNet) as the generator. Qualitative validation shows that noise generated by PNGAN is highly similar to real noise in terms of intensity and distribution. Quantitative experiments demonstrate that a series of denoisers trained with the generated noisy images achieve state-of-the-art (SOTA) results on four real denoising benchmarks.

PNGAN Framework

Main Results

Noise Generation : We adopt the two metrics used in DANet to measuere the performance of our generated noise, i.e., PSNR Gap (PGap) and Average KL Divergence (AKLD). The results are shown in the following table.

Noise Removal : The quantitative results of models finetuned with the image pairs generated by our PNGAN are listed in the following table. Our models achieve state-of-the-art results.

Create Environment

The model is built in PyTorch 1.1.0 and tested on Ubuntu 16.04 environment (Python3.7, CUDA9.0, cuDNN7.5).

For installing, follow these intructions

sudo apt-get install cmake build-essential libjpeg-dev libpng-dev
conda create -n pytorch1 python=3.7
conda activate pytorch1
conda install pytorch=1.7 torchvision=0.8.0 cudatoolkit=9.0 -c pytorch
pip install matplotlib scikit-image opencv-python yacs joblib natsort h5py tqdm

Or you can directly install

conda env create -f PNGAN.yaml

Noise Removal

Download our pre-trained models (Google Drive | Baidu Disk, code: 65m5) and place them into folder ./pre-trained following these instructions

# For SIDD denoising
python test_denoiser.py --method mirnet --input_dir ./datasets/SIDD/ --result_dir ./results/SIDD/noise_removal/ --weights ./pre-trained/MIRNet_sidd.pth

# For PolyU denoising
python test_denoiser.py --method mirnet --input_dir ./datasets/PolyU/ --result_dir ./results/PolyU/noise_removal/ --weights ./pre-trained/MIRNet_polyu.pth

# For Nam denoising
python test_denoiser.py --method ridnet --input_dir ./datasets/Nam/ --result_dir ./results/Nam/noise_removal/ --weights ./pre-trained/RIDNet_nam.pth

For DND denoising, we provide the denoised results on DND in folder ./results/DND/noise_removal/. The file is named in terms of id_PSNR_SSIM.png. Our method ranks the 4th place on the public leaderboard. We have also provided denoised results on SIDD, PolyU, and Nam.

for instance, on PolyU

the denoised results are in ./results/PolyU/noise_removal/

You can also refer to this repo for the application of PNGAN on Reformer.

Noise Generation

We provide the noisy images generated by PNGAN on SIDD, DND, PolyU, Nam, DF2K, Urban100, Kodak24, BSD68 and Gaussian noisy images (X50) for other works to compare their performance with PNGAN.

for instance, on SIDD

the generated noisy images are in ./results/SIDD/noise_modeling/

the Gaussian noisy images are in ./results/SIDD/Gaussian_noise/

And you can find the clean image counterparts in ./datasets/SIDD/groundtruth/, feel free to use your method to generate noisy images from the clean counterparts and make a convenient comparison with our PNGAN.

If you want to compare Gaussian noisy images at other level (e.g., X30, X70), run the matlab script:

run('Generate_TrainData_HQ_LQ_Denoising_RGB.m')

You can also refer to this repo for the application of PNGAN on Reformer.

Citation

@article{cai2021learning,
  title={Learning to generate realistic noisy images via pixel-level noise-aware adversarial training},
  author={Cai, Yuanhao and Hu, Xiaowan and Wang, Haoqian and Zhang, Yulun and Pfister, Hanspeter and Wei, Donglai},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={3259--3270},
  year={2021}
}

@inproceedings{pngan, 
  title={Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training}, 
  author={Cai, Yuanhao and Hu, Xiaowan and Wang, Haoqian and Zhang, Yulun and Pfister, Hanspeter and Wei, Donglai}, 
  booktitle={NeurIPS}, 
  year={2021}
}

Acknowledgement

The authors want to thank the following repo for their application of PNGAN on Reformer.

https://github.com/GarrickZ2/Image-Denoising

🚀 Quick Start

sudo apt-get install cmake build-essential libjpeg-dev libpng-dev
conda create -n pytorch1 python=3.7
conda activate pytorch1
conda install pytorch=1.7 torchvision=0.8.0 cudatoolkit=9.0 -c pytorch
pip install matplotlib scikit-image opencv-python yacs joblib natsort h5py tqdm