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

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 crowded dramatically improves fidelity of astrometric photometric measurements extracted from sky monitoring imagery. Our radically approach utilizes convolutional sparse representations to model continuous functions involved image formation. This avoids need detect precisely localize individual point sources that is shared by existing methods. In experiments involving simulated imagery, it significantly outperforms recent alternative method which compared.