Super-resolution method using sparse regularization for point-spread function recovery
This addresses the need for accurate instrument calibration in astronomy, such as for the ESA Euclid mission, but is incremental as it builds on existing super-resolution techniques.
The paper tackles the problem of undersampled images in large-scale spatial surveys by introducing SPRITE, a super-resolution algorithm using sparse regularization for point-spread function recovery, showing significant improvements over existing methods, particularly on low SNR PSFs.
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.