This article is missing information about how it works (Regularized Maximum Likelihood jargon idk).April 2019)(
CHIRP (Continuous High-resolution Image Reconstruction using Patch priors) is a Bayesian algorithm used to perform a deconvolution on images created in radio astronomy. The acronym was coined by lead author Katherine L. Bouman in 2016. 
The development of CHIRP involved a large team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory, the Harvard-Smithsonian Center for Astrophysics and the MIT Haystack Observatory, including Bill Freeman and Sheperd Doeleman.  It was first presented publicly by Bouman at the IEEE Computer Vision and Pattern Recognition conference in June 2016.
The CHIRP algorithm was developed to process data collected by the very-long-baseline Event Horizon Telescope, the international collaboration that in 2019 captured the black hole image of M87* for the first time. CHIRP was not used to produce the image,  but was an algebraic solution for the extraction of information from radio signals producing data by an array of radio telescopes scattered around the globe.  Stable sources (that don't change over short periods of time) can also gain signal by integrating the change at each location with the rotation of the earth. :915 Because the radio telescopes used in the project produce vast amounts of data, which contain gaps, the CHIRP algorithm is one of the ways to fill the gaps in the collected data. 
For reconstruction of such images which have sparse frequency measurements the CHIRP algorithm tends to outperform CLEAN, BSMEM (BiSpectrum Maximum Entropy Method), and SQUEEZE, especially for datasets with lower signal-to-noise ratios and for reconstructing images of extended sources. While the BSMEM and SQUEEZE algorithms may perform better with hand-tuned parameters, tests show CHIRP can do better with less user expertise.
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- Katherine L. Bouman, Michael D. Johnson, Daniel Zoran, Vincent L. Fish, Sheperd S. Doeleman, William T. Freeman (June 2016). "Computational Imaging for VLBI Image Reconstruction". IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2016: 913–922 – via Proceedings CVPR 2016 open access by Computer Vision Foundation.
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- The Event Horizon Telescope Collaboration. "First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole. Appendix A: Regularizer Definitions".
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- Kazunori Akiyama, Katherine L. Bouman, Andrew A. Chael, Michael D.Johnson, Sheperd S. Doeleman, Lindy Blackburn, John F. C. Wardle, William T. Freeman and the Event Horizon Telescope Collaboration, Vincent L. Fish (July 2017). "Observing—and Imaging—Active Galactic Nuclei with the Event Horizon Telescope". Galaxies. 4: 54 and 64. doi:10.3390/galaxies4040054 – via MDPI open access publishing.CS1 maint: Multiple names: authors list (link)