| |
 |
| Paper: |
A Flexible Expectation Maximization Framework for Fast, Scalable and High-fidelity Multi-frame Astronomical Image Deconvolution |
| Volume: |
538, ADASS XXXII |
| Page: |
150 |
| Authors: |
Yashil Sukurdeep; Fausto Navarro; Tamas Budavári |
| DOI: |
10.26624/XKFP1406 |
| 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 4k by 4k 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. |
|
|
|
|
|
