Abstract

Very long baseline interferometry (VLBI) is a techniquefor imaging celestial radio emissions by simultaneously ob-serving a source from telescopes distributed across Earth.The challenges in reconstructing images from fine angularresolution VLBI data are immense. The data is extremelysparse and noisy, thus requiring statistical image modelssuch as those designed in the computer vision community.In this paper we present a novel Bayesian approach forVLBI 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 tomembers of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons across algorithms.