PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem

TL;DR

This paper introduces a novel convolutional dictionary learning algorithm for estimating the PSF in crowded astronomical images, improving measurement accuracy without requiring source detection.

Contribution

The paper presents a new convolutional sparse representation method for PSF estimation that handles extreme crowding without source localization, outperforming existing techniques.

Findings

Significantly better PSF estimation accuracy in simulations

Outperforms recent alternative methods in crowded scenarios

Avoids the need for source detection and localization

Abstract

We present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations to model the continuous functions involved in the image formation. This approach avoids the need to detect and precisely localize individual point sources that is shared by existing methods. In experiments involving simulated astronomical imagery, it significantly outperforms the recent alternative method with which it is compared.