Keith Kelly (Emory University)
Keywords: Wavefront Reconstruction, Frozen Flow, MFBD, Parallel Algorithsm
Abstract:
Obtaining high resolution images of space objects from ground based telescopes is challenging, often requiring the use of a multi-frame blind deconvolution (MFBD) algorithm to remove blur caused by atmospheric turbulence. In order for an MFBD algorithm to be effective, it is necessary to obtain a good initial estimate of the wavefront phase. Although wavefront sensors work well in low turbulence situations, they are less effective in high turbulence, such as when imaging in daylight, or when imaging objects that are close to the Earths horizon. One promising approach, which has been shown to work very well in high turbulence settings, uses a frozen flow assumption on the atmosphere to capture the inherent temporal correlations present in consecutive frames of wavefront data. Exploiting these correlations can lead to more accurate estimation of the wavefront phase, and the associated PSF, which leads to more effective MFBD algorithms. However, with the current serial implementation, the approach can be prohibitively expensive in situations when it is necessary to use a large number of frames. In this poster we describe a parallel implementation that overcomes this constraint. The parallel implementation exploits sparse matrix computations, and uses the Trilinos package developed at Sandia National Laboratories. Trilinos provides a variety of core mathematical software for parallel architectures that have been designed using high quality software engineering practices, The package is open source, and portable to a variety of high-performance computing architectures.
Date of Conference: September 10-13, 2013
Track: Poster