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Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed tomography (CT) denoising. However, they require paired noisy-clean training data, which is difficult to obtain in practice. Some classic self-super
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