  We propose using a coordinate network decoder for the task of
super-resolution in MRI. The continuous signal representation of coordinate
networks enables this approach to be scale-agnostic, i.e. one can train over a
continuous range of scales and subsequently query at arbitrary resolutions. Due
to the difficulty of performing super-resolution on inherently noisy data, we
analyze network behavior under multiple denoising strategies. Lastly we compare
this method to a standard convolutional decoder using both quantitative metrics
and a radiologist study implemented in Voxel, our newly developed tool for
web-based evaluation of medical images.
