Image analysis algorithms for critically sampled curvature wavefront sensor images in the presence of large intrinsic aberrations

Nirmal Bissonauth; Paul Clark; Gavin B. Dalton; Richard M. Myers; William J. Sutherland

doi:10.1117/12.550063

15 September 2004 Image analysis algorithms for critically sampled curvature wavefront sensor images in the presence of large intrinsic aberrations

Nirmal Bissonauth, Paul Clark, Gavin B. Dalton, Richard M. Myers, William J. Sutherland

Author Affiliations +

Proceedings Volume 5496, Advanced Software, Control, and Communication Systems for Astronomy; (2004) https://doi.org/10.1117/12.550063
Event: SPIE Astronomical Telescopes + Instrumentation, 2004, Glasgow, United Kingdom

Abstract

This paper describes the image analysis algorithm developed for VISTA to recover wavefront information from curvature wave front sensor images. This technique is particularly suitable in situations where the defocused images have a limited number of pixels and the intrinsic or null aberrations contribute significantly to distort the images. The algorithm implements the simplex method of Nelder and Mead. The simplex algorithm generates trial wavefront coefficients that are fed into a ray tracing algorithm which in turn produces a pair of defocused images. These trial defocused images are then compared against the images obtained from a sensor, using a fitness function. The value returned from the fitness function is fed back to the simplex algorithm, which then decides how the next set of trial coefficients is produced.

Citation Download Citation

Nirmal Bissonauth, Paul Clark, Gavin B. Dalton, Richard M. Myers, and William J. Sutherland "Image analysis algorithms for critically sampled curvature wavefront sensor images in the presence of large intrinsic aberrations", Proc. SPIE 5496, Advanced Software, Control, and Communication Systems for Astronomy, (15 September 2004); https://doi.org/10.1117/12.550063

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