  Very long baseline interferometry (VLBI) is a technique for imaging celestial
radio emissions by simultaneously observing a source from telescopes
distributed across Earth. The challenges in reconstructing images from fine
angular resolution VLBI data are immense. The data is extremely sparse and
noisy, thus requiring statistical image models such as those designed in the
computer vision community. In this paper we present a novel Bayesian approach
for VLBI image reconstruction. While other methods often require careful tuning
and parameter selection for different types of data, our method (CHIRP)
produces good results under different settings such as low SNR or extended
emission. The success of our method is demonstrated on realistic synthetic
experiments as well as publicly available real data. We present this problem in
a way that is accessible to members of the community, and provide a dataset
website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons
across algorithms.
