Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceRecovery with a self-calibrated denoiser (ReSiDe) is an unsupervised learning method based on the plug-and-play (PnP) framework. In ReSiDe, denoiser training and a call to the denoising subroutine are performed in each iteration of PnP. However, ReSiDe is computationally slow, and its performance is sensitive to the noise level selected to train the denoiser. Here, we extend ReSiDe from single-image to multi-image recovery (ReSiDe-M), improving both performance and computation speed. We also propose an auto-tuning method to select the noise level for denoiser training. Using data from fastMRI and MRXCAT perfusion phantom, we compare ReSiDe-M with other unsupervised methods.
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