A flexible Expectation-Maximization framework for fast, scalable and high-fidelity multi-frame astronomical image deconvolution

TL;DR

This paper introduces a fast, scalable expectation-maximization framework for multi-frame astronomical image deconvolution that leverages GPU acceleration, enabling high-fidelity reconstructions of large-scale survey data.

Contribution

It presents a flexible TensorFlow-based EM method optimized for large astronomical datasets, improving deconvolution speed and quality with GPU support.

Findings

High-fidelity reconstruction of the night sky

Recovery of detailed galaxy structures

Perfect deconvolution of stars into single pixels

Abstract

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.