Superresolving sentinel-2 using learned multispectral regularization

The Sentinel-2 (S2) satellite constellation provides images at three different spatial resolutions and model based superresolution methods have proved useful for sharpening them to their maximum resolution. Algorithm unrolling is a way of building efficient, interpretable neural networks by reimplementing traditional algorithms in a neural network context. In this paper, an unrolled model based method to superresolve S2 images is proposed and unsupervised single image training is performed using reduced scale data. The method is evaluated using both real and simulated data.