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

Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks