In large-scale spatial surveys, such as the forthcoming ESA Euclid mission, images may be undersampled due to the optical sensors sizes. Therefore, one may consider using a super-resolution (SR) method to recover aliased frequencies, prior to further analysis. This is particularly relevant for point-source images, which provide direct measurements of the instrument point-spread function (PSF). We introduce SPRITE, SParse Recovery of InsTrumental rEsponse, which is an SR algorithm using a sparse analysis prior. We show that such a prior provides significant improvements over existing methods, especially on low SNR PSFs.
@article{arxiv.1410.7679,
title = {Super-resolution method using sparse regularization for point-spread function recovery},
author = {Fred Maurice Ngolè Mboula and Jean-Luc Starck and Samuel Ronayette and Koryo Okumura and Jérôme Amiaux},
journal= {arXiv preprint arXiv:1410.7679},
year = {2014}
}