Jolideco is a Python package for Joint Likelihood Deconvolution of astronomical images affected by Poisson noise. It allows you to deblur and denoise images and do a joint image reconstruction of multiple images from different instruments, while taking their specific instrument response functions, such as point spread functions, exposure and instrument specific background emission into account. To ensure a high fidelity of reconstructed features in the images, Jolideco relies on a patch based image prior, which is based on a Gaussian Mixture Model (GMM).

Jolideco is an open-source project and we welcome contributions of all kinds: new features, bug fixes, documentation improvements, and more. If you are interested in contributing, please get in contact with the maintainers and make sure to read the Code of Conduct.

When using Jolideco, please cite the version you used from Zenodo and the following paper reference:

@article{Jolideco2024,
    doi = {10.3847/1538-3881/ad6b98},
    url = {https://dx.doi.org/10.3847/1538-3881/ad6b98},
    year = {2024},
    month = {sep},
    publisher = {The American Astronomical Society},
    volume = {168},
    number = {4},
    pages = {182},
    author = {Axel Donath and Aneta Siemiginowska and Vinay L. Kashyap and David A. van Dyk and Douglas Burke},
    title = {Joint Deconvolution of Astronomical Images in the Presence of Poisson Noise},
    journal = {The Astronomical Journal},
}

Please also take a look at the following associated repositories:

• Jolideco GMM Library
• Jolideco Fermi-LAT Example
• Jolideco Chandra Example
• Webpage with Result Comparisons for Toy Datasets
• Jolideco Performance Benchmarks

While contributions are welcome in general, currently I cannot review PRs, nor help with implementations, because of a lack of time. So PRs are unlikely to get merged. However any kind of bug report or feature requests are welcome as well.