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Deep learning methods have shown remarkable performance in image denoising,particularly when trained on large-scale paired datasets. However, acquiringsuch paired datasets for real-world scenarios poses a significant challenge.Although unsupervised approaches based on generative adversarial networks offera promising solution for denoising without paired datasets, they are difficultin surpassing the performance limitations of conventional GAN-basedunsupervised frameworks without significantly modifying existing structures orincreasing the computational complexity of denoisers. To address this problem,we propose a SC strategy for multiple denoisers. This strategy can achievesignificant performance improvement without increasing the inference complexityof the GAN-based denoising framework. Its basic idea is to iteratively replacethe previous less powerful denoiser in the filter-guided noise extractionmodule with the current powerful denoiser. This process generates bettersynthetic clean-noisy image pairs, leading to a more powerful denoiser for thenext iteration. This baseline ensures the stability and effectiveness of thetraining network. The experimental results demonstrate the superiority of ourmethod over state-of-the-art unsupervised methods.

Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches