Nuit Blanche: Challenge: Computational Imaging for VLBI Image Reconstruction

Thursday, December 10, 2015

Challenge: Computational Imaging for VLBI Image Reconstruction

Here is a reconstruction endeavor that could eventually help the Event Horizon Telescope: Computational Imaging for VLBI Image Reconstruction by Katherine L. Bouman, Michael D. Johnson, Daniel Zoran, Vincent L. Fish, Sheperd S. Doeleman, William T. Freeman

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 require careful tuning and parameter selection for different types of images, our method is robust and produces good results under different settings such as low SNR or extended emissions. 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 computer vision community, and provide a dataset website (vlbiimaging.csail.mit.edu) to allow for controlled comparisons across algorithms. This dataset can foster development of new methods by making VLBI easily approachable to computer vision researchers.

At vlbiimaging.csail.mit.edu, they have a Dataset Designed to Train and Test Very Long Baseline Interferometry Image Reconstruction Algorithms. From the first page:

Welcome to the VLBI Reconstruction Dataset!
The goal of this website is to provide a testbed for developing new VLBI reconstruction algorithms. By supplying a large set of easy to understand training and testing data, we hope to make the problem more accessible to those less familiar with the VLBI field. Specifically, this website contains a:

Large set of synthetic training data for many different VLBI arrays and targets

Set of real data measurements provided in the same standard format

Standardized data set for testing VLBI Image Reconstruction Algorithms

Online quantitative evaluation of algorithm performance on simulated testing data

Qualitative comparison of algorithm performance on the reconstruction of real data

Online form to easily simulate realistic data using your own image and telescope parameters

What is VLBI Imaging?
Imaging distant celestial sources with high resolving power requires telescopes with prohibitively large diameters due to the inverse relationship between angular resolution and telescope diameter. However, by simultaneously collecting data from an array of telescopes located around the Earth, it is possible to emulate samples from a single telescope with a diameter equal to the maximum distance between telescopes in the array. Using multiple telescopes in this manner is referred to as very long baseline interferometry (VLBI).
Reconstructing an image using VLBI measurements is an ill-posed problem, and as such each there are an infinite number of possible images that explain the data. The challenge is to find an explanation that respects these prior assumptions while still satisfying the observed data. The goal of this website to aid in the process of developing these algorithms as well as evaluate their performance.

The ongoing international effort to create an Event Horizon Telescope capable of imaging the enviroment around a black hole’s event horizon calls for the use of VLBI reconstruction algorithms. The angular resolution necessary for these measurements requires overcoming many challenges, all of which make image reconstruction more difficult. For instance, at the mm/sub-mm wavelengths being observed, rapidly varying inhomogeneities in the atmosphere introduce additional measurement errors. Robust algorithms that are able to reconstruct images in this fine angular resolution regime are essential for scientific progress.