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

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.

References in corpus (9)

• Weak lensing for precision cosmology
• Toward High-Precision Astrometry with WFPC2. I. Deriving an Accurate PSF
• Convolutional Dictionary Learning: A Comparative Review and New Algorithms
• PSF modelling for very wide-field CCD astronomy
• Fast Convolutional Dictionary Learning off the Grid
• Constraint matrix factorization for space variant PSFs field restoration
• Group Invariant Dictionary Learning
• PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem
• Alternating minimization for dictionary learning: Local Convergence Guarantees

Cited by in corpus (3)

• Efficient ADMM-based Algorithms for Convolutional Sparse Coding
• PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem
• Gaussian Process Convolutional Dictionary Learning