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Super resolution is an essential tool in optics, especially on interstellarscales, due to physical laws restricting possible imaging resolution. Wepropose using optimal transport and entropy for super resolution applications.We prove that the reconstruction is accurate when sparsity is known and noiseor distortion is small enough. We prove that the optimizer is stable and robustto noise and perturbations. We compare this method to a state of the artconvolutional neural network and get similar results for much lesscomputational cost and greater methodological flexibility.

Optimal Transport for Super Resolution Applied to Astronomy Imaging