Joint multiband deconvolution for Euclid and Vera C. Rubin images
This provides a versatile solution for astrophysicists to enhance image resolution in surveys, though it is incremental as it builds on existing deconvolution techniques with a novel joint approach.
The paper tackles the problem of improving the resolution of ground-based astronomical images by jointly deconvolving multiband data from Euclid and Vera C. Rubin surveys, leveraging correlations between overlapping bands to achieve higher resolution comparable to space-based observations.
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.