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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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AI Tool
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.
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Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising vs Lev8
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Open route →Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches vs Lev8
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Open route →Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising vs Triple 3D
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Open route →Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches vs Triple 3D
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Open route →Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising vs SpringBrand
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