Keywords: Data Processing, Modelling, Deep learning, DenoiseIn this work, we propose a new denoising method named Cross-Subject Transformer Denoising (CSTD), which transfers the texture of a reference image retrieved from a large database to the noisy image with soft attention mechanisms. The experiments on the fastMRI dataset with various noise levels show that our method is likely superior to many competing denoising algorithms including current the state-of-the-art NAFNet. Moreover, our method exhibits excellent generalizability when directly applied to in-vivo low-field data without retraining. Due to the flexibility, the method is expected to have a wide range of applications.
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