  Super-resolution using deep neural networks typically relies on highly
curated training sets that are often unavailable in clinical deployment
scenarios. Using loss functions that assume Gaussian-distributed residuals
makes the learning sensitive to corruptions in clinical training sets. We
propose novel loss functions that are robust to corruptions in training sets by
modeling heavy-tailed non-Gaussian distributions on the residuals. We propose a
loss based on an autoencoder-based manifold-distance between the super-resolved
and high-resolution images, to reproduce realistic textural content in
super-resolved images. We propose to learn to super-resolve images to match
human perceptions of structure, luminance, and contrast. Results on a large
clinical dataset shows the advantages of each of our contributions, where our
framework improves over the state of the art.
