Ground-based image deconvolution with Swin Transformer UNet

TL;DR

This paper presents a novel two-step ground-based image deconvolution framework using Swin Transformer UNet, improving resolution and efficiency while addressing bias issues through a sparsity wavelet approach, with promising applications in astronomy.

Contribution

Introduces a two-step deep learning deconvolution method with a novel third step to reduce bias, enhancing resolution and generalization in astronomical images.

Findings

Outperforms classical algorithms like Firedec in resolution recovery

Demonstrates robustness to different noise properties

Enables analysis of galaxy structures in ground-based images

Abstract

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen the understanding of galaxy formation and evolution through accurate photometric measurements. We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. To address this limitation, we propose a novel third step relying on the active coefficients in the sparsity wavelet framework. We conducted a performance comparison between our deep learning-based method and Firedec, a classical deconvolution algorithm, based on an analysis of a subset of the EDisCS cluster samples. We demonstrate the advantage of our method in terms of resolution recovery, generalisation to different noise properties, and computational efficiency. The analysis of this cluster sample not only allowed us to assess the efficiency of our method, but it also enabled us to quantify the number of clumps within these galaxies in relation to their disc colour. This robust technique that we propose holds promise for identifying structures in the distant universe through ground-based images.