  We present a computationally efficient expectation-maximization framework for
multi-frame image deconvolution and super-resolution. Our method is well
adapted for processing large scale imaging data from modern astronomical
surveys. Our Tensorflow implementation is flexible, benefits from advanced
algorithmic solutions, and allows users to seamlessly leverage Graphical
Processing Unit (GPU) acceleration, thus making it viable for use in modern
astronomical software pipelines. The testbed for our method is a set of $4$K by
$4$K Hyper Suprime-Cam exposures, which are closest in terms of quality to
imaging data from the upcoming Rubin Observatory. The preliminary results are
extremely promising: our method produces a high-fidelity non-parametric
reconstruction of the night sky, from which we recover unprecedented details
such as the shape of the spiral arms of galaxies, while also managing to
deconvolve stars perfectly into essentially single pixels.
