Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means.
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
We argue that in doing so, the challenge ranks the proposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to the high-resolution target.
We propose a self-supervised approach for training multi-frame video denoising networks.
New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities.
In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency.
We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR.