Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising vs Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches

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Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising

Note that we use LDCT images based on the noisy-as-clean strategy for corruption instead of NDCT images.

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

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.