Learning the Night Sky with Deep Generative Priors
This addresses the challenge of improving image quality in ground-based astronomy, particularly for observatories like Rubin, but appears incremental as it builds on deep generative priors.
The paper tackles the problem of recovering sharper images from blurred astronomical observations by developing an unsupervised multi-frame method for denoising, deblurring, and coadding images, yielding promising restored images and extracted source lists from 4K by 4K Hyper Suprime-Cam exposures.
Recovering sharper images from blurred observations, referred to as deconvolution, is an ill-posed problem where classical approaches often produce unsatisfactory results. In ground-based astronomy, combining multiple exposures to achieve images with higher signal-to-noise ratios is complicated by the variation of point-spread functions across exposures due to atmospheric effects. We develop an unsupervised multi-frame method for denoising, deblurring, and coadding images inspired by deep generative priors. We use a carefully chosen convolutional neural network architecture that combines information from multiple observations, regularizes the joint likelihood over these observations, and allows us to impose desired constraints, such as non-negativity of pixel values in the sharp, restored image. With an eye towards the Rubin Observatory, we analyze 4K by 4K Hyper Suprime-Cam exposures and obtain preliminary results which yield promising restored images and extracted source lists.