Paper Info
Title
Computational Imaging For Vlbi Image Reconstruction
Abstract
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.
Year
DOI
Venue
2015
10.1109/CVPR.2016.105
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Field
DocType
Volume
Iterative reconstruction,Computational photography,Optics,Angular resolution,Data type,Chirp,Very-long-baseline interferometry,Bayesian probability,Physics
Journal
abs/1512.01413
Issue
ISSN
Citations 
1
1063-6919
2
PageRank 
References 
Authors
0.39
6
6
Name
Order
Citations
PageRank
Katherine L. Bouman11197.97
Michael D. Johnson231.09
Daniel Zoran3856.59
Vincent L. Fish420.39
Sheperd S. Doeleman5314.68
William T. Freeman6173821968.76