Computational Imaging for VLBI Image Reconstruction
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 no...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Online Access: | http://hdl.handle.net/1721.1/103077 https://orcid.org/0000-0003-4988-9771 https://orcid.org/0000-0002-2231-7995 https://orcid.org/0000-0003-0077-4367 |
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| author | Bouman, Katherine L. Johnson, Michael D. Zoran, Daniel Fish, Vincent L. Doeleman, Sheperd Samuel Freeman, William T. |
| author2 | Haystack Observatory |
| author_facet | Haystack Observatory Bouman, Katherine L. Johnson, Michael D. Zoran, Daniel Fish, Vincent L. Doeleman, Sheperd Samuel Freeman, William T. |
| author_sort | Bouman, Katherine L. |
| collection | MIT |
| description | 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 algorithm. |
| first_indexed | 2024-09-23T14:20:45Z |
| format | Article |
| id | mit-1721.1/103077 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:20:45Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1030772022-10-01T20:44:59Z Computational Imaging for VLBI Image Reconstruction Bouman, Katherine L. Johnson, Michael D. Zoran, Daniel Fish, Vincent L. Doeleman, Sheperd Samuel Freeman, William T. Haystack Observatory Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Bouman, Katherine L. Bouman, Katherine L. Zoran, Daniel Fish, Vincent L. Doeleman, Sheperd Samuel Freeman, William T. 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 algorithm. National Science Foundation (U.S.) (CGV-1111415) National Science Foundation (U.S.). Graduate Research Fellowship National Science Foundation (U.S.) (AST-1310896) National Science Foundation (U.S.) (AST-1211539) National Science Foundation (U.S.) (AST-1440254) Gordon and Betty Moore Foundation (GBMF-3561) 2016-06-09T14:40:46Z 2016-06-09T14:40:46Z 2016-06 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/103077 Bouman, Katherine L., Michael D. Johnson, Daniel Zoran, Vincent L. Fish, Sheperd S. Doeleman, and William T. Freeman. "Computational Imaging for VLBI Image Reconstruction." 2016 IEEE Conference on Computer Vision and Pattern Recognition (June 2016). https://orcid.org/0000-0003-4988-9771 https://orcid.org/0000-0002-2231-7995 https://orcid.org/0000-0003-0077-4367 en_US http://cvpr2016.thecvf.com/program/main_conference Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Bouman |
| spellingShingle | Bouman, Katherine L. Johnson, Michael D. Zoran, Daniel Fish, Vincent L. Doeleman, Sheperd Samuel Freeman, William T. Computational Imaging for VLBI Image Reconstruction |
| title | Computational Imaging for VLBI Image Reconstruction |
| title_full | Computational Imaging for VLBI Image Reconstruction |
| title_fullStr | Computational Imaging for VLBI Image Reconstruction |
| title_full_unstemmed | Computational Imaging for VLBI Image Reconstruction |
| title_short | Computational Imaging for VLBI Image Reconstruction |
| title_sort | computational imaging for vlbi image reconstruction |
| url | http://hdl.handle.net/1721.1/103077 https://orcid.org/0000-0003-4988-9771 https://orcid.org/0000-0002-2231-7995 https://orcid.org/0000-0003-0077-4367 |
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