Joint multi-band deconvolution for Euclid and Vera C. Rubin images

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 capitalises on the fortunate fact that the Vera C. Rubin (LSST) $r$-, $i$-, 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.

The algorithm is described in detail in Akhaury et al. (2025).

Installation

1. Download and install Miniconda. Choose the Python 3.x version for your platform.

2. Open a Terminal (Linux/macOS) or Command Prompt (Windows) and run the following commands:

        conda update conda
        conda install git
        git clone https://github.com/utsav-akhaury/Multiband-Deconvolution
        cd Multiband-Deconvolution

3. Create a conda environment and install all the required dependencies by running the following commands:

        conda env create -f conda_env.yml

Code Overview

    Multiband-Deconvolution/
        Data/
            deconv_result.png
            euclid.npy
            noisemap_LSST.npy
            noisy_LSST.npy
            psf_euclid_vis.npy
            psf_LSST.npy
            sed.npy
            target_HST.npy
        README.md
        conda_env.yml
        MBDeconv_FISTA.py
        run_MCDeconv.ipynb

• Data is the directory containing the test images used in the tutorial notebook.
  • deconv_result.png is an image of a deconvolved galaxy.
  • euclid.npy is the Euclid $VIS$-band image.
  • noisemap_LSST.npy is the noise map of the LSST $r$-, $i$-, and $z$-band images.
  • noisy_LSST.npy is the low-resolution LSST image in $r$-, $i$-, and $z$-bands.
  • psf_euclid_vis.npy is the Euclid $VIS$-band PSF.
  • psf_LSST.npy is the LSST PSF in each LSST band at Euclid resolution.
  • sed.npy is the fractional contribution of each LSST band to the Euclid VIS band.
  • target_HST.npy is the target high-resolution HST image.
• README.md contains getting started information on installation and usage.
• conda_env.yml is a configuration file for Anaconda (Miniconda) that sets up a Python environment with all the required Python packages for using the Multi-band Deconvolution code.
• MBDeconv_FISTA.py contains the implementation of the Multi-band Deconvolution algorithm.
• run_MBDeconv.ipynb is a Jupyter notebook that demonstrates an example of how to deconvolve the simulated LSST images using the Euclid VIS-band image as a high-resolution prior.

Usage

1. Activate the mbdeconv conda environment:

        conda activate mbdeconv

2. Run the run_MBDeconv.ipynb notebook, which will guide you through the deconvolution process.