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Denoise X: Self-Supervised U-Net for Quantum Mottle Suppression in Chest Radiography
Deep learning for medical image restoration is often constrained by the need for paired training data. Acquiring perfectly registered clean-noisy radiographs in vivo is impractical due to patient motion and radiation-dose limits. To address this, we employ a self-supervised Noise2Noise paradigm that
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