Unsupervised Image Denoising in Real-World Scenarios via
  Self-Collaboration Parallel Generative Adversarial Branches
Xin Lin Chao Ren Xiao Liu Jie Huang Yinjie Lei
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 offer
a promising solution for denoising without paired datasets, they are difficult
in surpassing the performance limitations of conventional GAN-based
unsupervised frameworks without significantly modifying existing structures or
increasing the computational complexity of denoisers. To address this problem,
we propose a 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. 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.