A flexible Expectation-Maximization framework for fast, scalable and high-fidelity multi-frame astronomical image deconvolution
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 K by 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.
Cite
@article{arxiv.2302.05804,
title = {A flexible Expectation-Maximization framework for fast, scalable and high-fidelity multi-frame astronomical image deconvolution},
author = {Yashil Sukurdeep and Fausto Navarro and Tamas Budavari},
journal= {arXiv preprint arXiv:2302.05804},
year = {2025}
}