Joint multiband deconvolution for Euclid and Vera C. Rubin images

TL;DR

This paper introduces a novel multiband deconvolution method that enhances ground-based astronomical images by jointly leveraging high-resolution space-based data, improving resolution, morphology, and flux preservation across multiple bands.

Contribution

The paper presents a new joint deconvolution algorithm that uses correlations between bands and deep learning denoising to improve ground-based image resolution with space-based observations.

Findings

Enhanced resolution and morphology recovery demonstrated.

Effective flux preservation across bands.

Method generalizes to various space-ground image combinations.

Abstract

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of both worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work we introduce a novel multiband deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the Rubin $r$, $i$, and $z$ bands lie within the Euclid VIS band. The algorithm jointly de-convolves all the data to convert the $r$-, $i$-, and $z$-band Rubin images to the resolution of Euclid by leveraging the correlations between the different bands. We also investigate the performance of deep-learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific Euclid-Rubin combination, offering a versatile solution to improving the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.