Computer Graphics
TU Braunschweig

Radio Astronomy Synthesis Imaging

Abstract

Radio interferometers sample an image of the sky in the spatial frequency domain. Reconstructing the image from a necessarily incomplete set of samples is an ill-posed inverse problem that we address with methods inspired by the theory of compressed sensing.

During two research visits to the National Radio Astronomy Observatory (NRAO) and the University of New Mexico, both gratefully funded by the Alexander von Humboldt Foundation, we had the unique opportunity to work together with world-leading experts in radio astronomy synthesis imaging to develop new algorithms for the Very Large Array (VLA) and other radio telescope arrays.


blob test scene and reconstructions

From left to right: dynamic 'blob' test scene, static reconstruction, independent reconstruction of time frames, temporally smooth reconstruction, reconstruction with sparse temporal variation.

point sources test scene and reconstructions

From left to right: dynamic 'point sources' test scene, static reconstruction, independent reconstruction of time frames, temporally smooth reconstruction, reconstruction with sparse temporal variation.

Publications

Stephan Wenger, Marcus Magnor:
A Sparse Reconstruction Algorithm for Multi-Frequency Radio Images
Technical Report no. 20, Inst. f. Computergraphik, TU Braunschweig, November 2014.

Dirk Lorenz, Stephan Wenger, Frank Schöpfer, Marcus Magnor:
A Sparse Kaczmarz Solver and a Linearized Bregman Method for Online Compressed Sensing
in Proc. IEEE International Conference on Image Processing (ICIP), pp. 1347-1351, October 2014.

Stephan Wenger, Urvashi Rau, Marcus Magnor:
A Group Sparsity Imaging Algorithm for Transient Radio Sources
in Astronomy and Computing, pp. 1-4, September 2013.

Stephan Wenger, Urvashi Rau, Marcus Magnor:
A Group Sparsity Imaging Algorithm for Transient Radio Sources
in Astronomy and Computing, vol. 1, pp. 40-45, February 2013.
http://dx.doi.org/10.1016/j.ascom.2013.02.002

Stephan Wenger, Marcus Magnor, Ylva Pihlström, Sanjay Bhatnagar, Urvashi Rau:
SparseRI: A Compressed Sensing Framework for Aperture Synthesis Imaging in Radio Astronomy
in Publications of the Astronomical Society of the Pacific (PASP), vol. 122, no. 897, pp. 1367-1374, October 2010.

Stephan Wenger, Soheil Darabi, Pradeep Sen, Karl-Heinz Glassmeier, Marcus Magnor:
Compressed Sensing for Aperture Synthesis Imaging
in Proc. IEEE International Conference on Image Processing (ICIP), pp. 1381-1384, September 2010.


Related Projects

Astrophysical Modeling and Visualization

Humans have been fascinated by astrophysical phenomena since prehistoric times. But while the measurement and image acquisition devices have evolved enormously by now, many restrictions still apply when capturing astronomical data. The most notable limitation is our confined vantage point in the solar system, disallowing us to observe distant objects from different points of view.

In an interdisciplinary German-Mexican research project partially funded by German DFG (Deutsche Forschungsgemeinschaft, grants MA 2555/7-1 and 444 MEX-113/25/0-1) and Mexican CONACyT (Consejo Nacional de Ciencia y Tecnología, grants 49447 and UNAM DGAPA-PAPIIT IN108506-2), we evaluate different approaches for automatical reconstruction of plausible three-dimensional models of planetary nebulae. The team comprises astrophysicists working on planetary nebula morphology as well as computer scientists experienced in the field of reconstruction and visualization of astrophysical objects.

Lunar Surface Relief Reconstruction

Our "Astrographics" research group works on various methods to overcome the difficulties associated with gaining knowledge about faraway astronomical objects using computer vision and computer graphics algorithms. In this project, we have computed plausible 3D surface data for the moon from photographic imagery from the 1960's "Lunar Orbiter" mission.