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.

基于生成式对抗网络的无监督方法在图像去噪方面取得了优异的性能，而我们提出的基于滤波引导噪声提取模块的深度学习策略能够在不增加推理复杂性的情况下显著提高去噪框架的性能。