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【ICCV 2023】 Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches

Xin Lin, Chao Ren, Xiao Liu, Jie Huang, Yinjie Lei

paper

The journal version is here RSCP^2^GAN.

This is the official code of SCPGabNet.

main_fig

Abstract

Deep learning methods have shown remarkable performance in image denoising, particularly when trained on large-scale paired datasets. However, acquiring such paired datasets for real-world scenarios poses a significant challenge. Although unsupervised approaches based on generative adversarial networks (GANs) offer a promising solution for denoising without paired datasets, they are difficult to surpass the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increase the computational complexity of denoisers. To address this problem, we propose a self-collaboration (SC) strategy for multiple denoisers. This strategy can achieve significant performance improvement without increasing the inference complexity of the GAN-based denoising framework. Its basic idea is to iteratively replace the previous less powerful denoiser in the filter-guided noise extraction module with the current powerful denoiser. This process generates better synthetic clean-noisy image pairs, leading to a more powerful denoiser for the next iteration. In addition, we propose a baseline method that includes parallel generative adversarial branches with complementary “self-synthesis” and “unpaired-synthesis” constraints. This baseline ensures the stability and effectiveness of the training network. The experimental results demonstrate the superiority of our method over state-of-the-art unsupervised methods.

Requirements

Our experiments are done with:

Dateset

SIDD Train: https://pan.baidu.com/s/1c1iPIIJvSfq6s6_M7iyjPA 2oe5

Test: https://pan.baidu.com/s/1yltsD684qpJa0SMJ9SdR5w 8qzf

Pre-trained Models

Google Drive: https://drive.google.com/file/d/1wzceTFvnoepftIEJn1DI73iHnGi_pvMl/view?usp=sharing

Baidu Drive: https://pan.baidu.com/s/1EdXN7o9EW_ssDRHxDKFeXw icp1

Train & Test

You can get the complete SIDD validation dataset from https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php.

'.mat' files need to be converted to images ('.png').

train and test are both in train_v6.py.

run trainv6.py.

Citation

@inproceedings{scpgabnet,
  title={Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches}, 
  author={Xin Lin and Chao Ren and Xiao Liu and Jie Huang and Yinjie Lei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

Contact

If you have any questions, please contact linxin@stu.scu.edu.cn