Optimal Transport for Super Resolution Applied to Astronomy Imaging
This addresses the challenge of improving imaging resolution in astronomy, where physical limits restrict resolution, offering a computationally efficient alternative to existing methods.
The paper tackles the problem of super resolution in astronomy imaging by proposing an optimal transport and entropy-based method, achieving similar results to a state-of-the-art convolutional neural network with much less computational cost and greater flexibility.
Super resolution is an essential tool in optics, especially on interstellar scales, due to physical laws restricting possible imaging resolution. We propose using optimal transport and entropy for super resolution applications. We prove that the reconstruction is accurate when sparsity is known and noise or distortion is small enough. We prove that the optimizer is stable and robust to noise and perturbations. We compare this method to a state of the art convolutional neural network and get similar results for much less computational cost and greater methodological flexibility.