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

arXiv: https://arxiv.org/abs/1410.7679 · HTML: https://mlsanity.com/paper/1410.7679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2014.

## Built on by (most cited first, 1 of 1)

- [Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies](https://mlsanity.com/paper/2306.07996/) (2023)

## Abstract (arXiv)

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.