Accelerating MRI is intrinsically limited by the thermal noise from the imaged object. In this work we aim to optimize MR image denoising using an unsupervised deep learning-based method. Stein's unbiased risk estimator and spatially resolved noise maps indicating the standard deviation of the noise for every pixel were incorporated into the training process. It was shown that this approach can achieve results that are equal or superior to those of state-of-the-art supervised and unsupervised methods. Furthermore, we show how to control the tradeoff between denoising and image sharpness by using a model conditioned on the noise map.
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