The following paper provides a general introduction to image reconstruction for interferometric data. The authors discuss the effects of different regularization schemes and provide a comprehensive review of existing algorithms.
Principles of image reconstruction in optical interferometry: tutorial. Thiébaut, É. and Young, J. J. Opt Soc. Am. A, 34, 904 (2017)
OIFITS-SIM - Observation Simulator
The OIFITS simulator is a tool to assist in observation planning or in reconstructed image artifact analysis. The program has the ability to either (1) simulate the data from an interferometer given a list of observational hour angles and a FITS image of what the source looks like, or (2) copy the UV coverage of an existing observation and simulate the data given a FITS image of the source.
SQUEEZE - Image reconstruction software
SQUEEZE is an image reconstruction software package for optical interferometry developed by Fabien Baron of Georgia State University and distributed under an open source (GPL v3) license. It is designed to image complex astrophysical sources, while (optionally) modeling them simultaneously with analytic models. SQUEEZE is based on Markov Chain Monte-Carlo (MCMC) exploration of the imaging probability space, and reconstructs images and associated error bars from standard OIFITS data. SQUEEZE leverages the Open Multi-Processing (OpenMP) application programming interface to implement simulated annealing and parallel tempering, in the hope of avoiding the local minima better than classic gradient-based image reconstruction software.
The BiSpectrum Maximum Entropy Method (BSMEM) is the image reconstruction software package developed at Cambridge. BSMEM applies a fully Bayesian approach to the inverse problem of finding the most probable image given the evidence. BSMEM makes use of the MemSys library created by Maximum Entropy Data Consultants Ltd. to implement a gradient descent algorithm for maximising the inference (posterior probability) of an image, using the entropy of the reconstructed image as the prior probability.
MACIM: The Markov Chain Imager
The MArkov Chain Imager (MACIM) is a publically available Monte Carlo imaging algorithm. The code is written in C, and should run under any Unix system (including Mac OS X).
The Multi-aperture Image Reconstruction Algorithm (MIRA) is an algorithm for image reconstruction from interferometric data. The software is written in Yorick. MiRA proceeds by direct minimization of a penalized likelihood. This penalty is the sum of two terms: a likelihood term which enforces agreement of the model with the data, plus a regularization term to account for priors. The priors are required to lever the many degeneracies due to the sparseness of the spatial frequency sampling. MiRA implements many different regularizations (quadratic or edge-preserving smoothness, total variation, maximum entropy, etc.) and lets the user defines their own priors. The likelihood penalty is modular and designed to account for available data of any kind (complex visibilities, powerspectra and/or closure phase). One of the strengths of MiRA is that it is purely based on an inverse problem approach and can therefore cope with incomplete data sets; for instance, MiRA can build an image without any Fourier phase information.