Mixture density knowledge distillation in super-resolution reconstruction of mri medical images

MUTED surpasses state-of-the-art (SOTA) methods with a reduced computational cost and outperforms competing knowledge distillation methods. By addressing numerical instability and leveraging the regularization constraint, MUTED offers a robust solution for high-quality image reconstruction. Furthermore, the aleatoric uncertainty formulated by the MDN serves to reveal sharpened boundaries. This, in turn, effectively facilitates the efficient enhancement of the super-resolution reconstruction quality.