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It allows you to deblur and denoise images and do a joint image reconstruction of\nmultiple images from different instruments, while taking their specific instrument response functions,\nsuch as point spread functions, exposure and instrument specific background emission into account.\nTo ensure a high fidelity of reconstructed features in the images, Jolideco relies on a patch based\nimage prior, which is based on a Gaussian Mixture Model (GMM). \n\nContributing Code, Documentation, or Feedback\n---------------------------------------------\nJolideco is an open-source project and we welcome contributions of all kinds: \nnew features, bug fixes, documentation improvements, and more. If you are interested\nin contributing, please get in contact with the maintainers and make sure to read the\n`Code of Conduct \u003chttps://github.com/jolideco/jolideco/blob/main/CODE_OF_CONDUCT.md\u003e`_.\n\nCitation\n--------\n\nWhen using Jolideco, please cite the `version you used from Zenodo \u003chttps://zenodo.org/doi/10.5281/zenodo.10870554\u003e`_ \nand the following paper reference:\n\n.. code-block:: bibtex\n\n    @article{Jolideco2024,\n        doi = {10.3847/1538-3881/ad6b98},\n        url = {https://dx.doi.org/10.3847/1538-3881/ad6b98},\n        year = {2024},\n        month = {sep},\n        publisher = {The American Astronomical Society},\n        volume = {168},\n        number = {4},\n        pages = {182},\n        author = {Axel Donath and Aneta Siemiginowska and Vinay L. Kashyap and David A. van Dyk and Douglas Burke},\n        title = {Joint Deconvolution of Astronomical Images in the Presence of Poisson Noise},\n        journal = {The Astronomical Journal},\n    }\n\n\n\nFurther Resources\n------------------\n\nPlease also take a look at the following associated repositories:\n\n- `Jolideco GMM Library \u003chttps://github.com/jolideco/jolideco-gmm-prior-library\u003e`_\n- `Jolideco Fermi-LAT Example \u003chttps://github.com/jolideco/jolideco-fermi-examples\u003e`_\n- `Jolideco Chandra Example \u003chttps://github.com/jolideco/jolideco-chandra-examples\u003e`_\n- `Webpage with Result Comparisons for Toy Datasets \u003chttps://jolideco.github.io/jolideco-comparison/\u003e`_\n- `Jolideco Performance Benchmarks \u003chttps://github.com/jolideco/jolideco-performance-benchmark\u003e`_\n\n\nContributing\n------------\nWhile contributions are welcome in general, currently I cannot review PRs, nor help with implementations,\nbecause of a lack of time. So PRs are unlikely to get merged. However any kind of bug report or feature\nrequests are welcome as well.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjolideco%2Fjolideco","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjolideco%2Fjolideco","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjolideco%2Fjolideco/lists"}