PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online blind deconvolution for Astronomy
S. Harmeling, M. Hirsch, S. Sra and B. Schölkopf
In: IEEE International Conference Computational Photography (ICCP 2009), 16-17 April 2009, San Francisco, USA.


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.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:4346
Deposited By:Bernhard Schölkopf
Deposited On:13 March 2009