With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images, and multi-band images. However, these two conditions are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of the two worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work, we introduce a novel multi-band 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 Vera C. Rubin r-, i-, and z-bands lie within the Euclid VIS band. The algorithm jointly deconvolves all the data to turn the r-, i-, and z-band Vera C. Rubin images to the resolution of Euclid by enabling us to leverage 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 improve the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters
