PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online Blind Image Deconvolution for Astronomy
Stefan Harmeling, Michael Hirsch, Suvrit Sra and Bernhard Schölkopf
In: IEEE International Conference on Computational Photography, 16-17 Apr 2009, San Francisco, USA.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6066
Deposited By:Stefan Harmeling
Deposited On:08 March 2010