Super-resolution method using sparse regularization for point-spread function recovery

TL;DR

This paper introduces SPRITE, a super-resolution algorithm utilizing sparse regularization to improve point-spread function recovery in undersampled images, especially effective in low SNR conditions.

Contribution

The paper presents SPRITE, a novel super-resolution method with a sparse analysis prior, enhancing PSF recovery over existing techniques in large-scale spatial surveys.

Findings

SPRITE outperforms existing methods in low SNR scenarios.

Sparse analysis prior significantly improves PSF recovery accuracy.

Method is effective for undersampled, large-scale spatial survey images.

Abstract

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.