Atmospheric turbulences blur astronomical images taken by earth-based telescopes. Taking many short-time exposures in such a situation provides noisy images of the same object, where each noisy image has a different blur. Commonly astronomers apply a technique called “Lucky Imaging” that selects a few of the recorded frames that fulfill certain criteria, such as reaching a certain peak intensity (“Strehl ratio”). The selected frames are then averaged to obtain a better image. In this paper we introduce and analyze a new method that exploits all the frames and generates an improved image in an online fashion. Our initial experiments with controlled artificial data and real-world astronomical datasets yields promising results.
Author(s): | Harmeling, S. and Hirsch, M. and Sra, S. and Schölkopf, B. |
Journal: | Proceedings of the First IEEE International Conference Computational Photography (ICCP 2009) |
Pages: | 1-7 |
Year: | 2009 |
Month: | April |
Day: | 0 |
Publisher: | IEEE |
Bibtex Type: | Conference Paper (inproceedings) |
Address: | Piscataway, NJ, USA |
DOI: | 10.1109/ICCPHOT.2009.5559014 |
Event Name: | First IEEE International Conference on Computational Photography (ICCP 2009) |
Event Place: | San Francisco, CA, USA |
Digital: | 0 |
Electronic Archiving: | grant_archive |
ISBN: | 978-1-4244-4534-9 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
BibTex
@inproceedings{5649, title = {Online blind deconvolution for astronomical imaging}, journal = {Proceedings of the First IEEE International Conference Computational Photography (ICCP 2009)}, abstract = {Atmospheric turbulences blur astronomical images taken by earth-based telescopes. Taking many short-time exposures in such a situation provides noisy images of the same object, where each noisy image has a different blur. Commonly astronomers apply a technique called “Lucky Imaging” that selects a few of the recorded frames that fulfill certain criteria, such as reaching a certain peak intensity (“Strehl ratio”). The selected frames are then averaged to obtain a better image. In this paper we introduce and analyze a new method that exploits all the frames and generates an improved image in an online fashion. Our initial experiments with controlled artificial data and real-world astronomical datasets yields promising results.}, pages = {1-7}, publisher = {IEEE}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Piscataway, NJ, USA}, month = apr, year = {2009}, slug = {5649}, author = {Harmeling, S. and Hirsch, M. and Sra, S. and Sch{\"o}lkopf, B.}, month_numeric = {4} }