  Reconstruction of magnetic resonance imaging (MRI) data has been positively
affected by deep learning. A key challenge remains: to improve generalisation
to distribution shifts between the training and testing data. Most approaches
aim to address this via inductive design or data augmentation. However, they
can be affected by misleading data, e.g. random noise, and cases where the
inference stage data do not match assumptions in the modelled shifts. In this
work, by employing a conditional hyperparameter network, we eliminate the need
of augmentation, yet maintain robust performance under various levels of
Gaussian noise. We demonstrate that our model withstands various input noise
levels while producing high-definition reconstructions during the test stage.
Moreover, we present a hyperparameter sampling strategy that accelerates the
convergence of training. Our proposed method achieves the highest accuracy and
image quality in all settings compared to baseline methods.
