Deconvolution from wavefront sensing with optimal wavefront estimation techniques

Scott R. Maethner; Michael C. Roggemann; Byron M. Welsh

doi:10.1117/12.279024

23 September 1997 Deconvolution from wavefront sensing with optimal wavefront estimation techniques

Scott R. Maethner, Michael C. Roggemann, Byron M. Welsh

Proceedings Volume 3125, Propagation and Imaging through the Atmosphere; (1997) https://doi.org/10.1117/12.279024
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Minimum variance wave front estimation techniques are used to improve Deconvolution from Wave front Sensing (DWFS), a method to mitigate the effects of atmospheric turbulence on imaging systems. Both least-squares and minimum variance wave front phase estimation techniques are investigated, using both Gaussian and Zernike polynomial elementary functions. Imaging simulations and established performance metrics are used to evaluate these wave front estimation techniques for a one-meter optical telescope. Results show that the minimum variance estimation technique that employs Zernike polynomial elementary functions provides the best mean and signal-to-noise ratio performance of all the investigated wave front estimation techniques.

Citation Download Citation

Scott R. Maethner, Michael C. Roggemann, and Byron M. Welsh "Deconvolution from wavefront sensing with optimal wavefront estimation techniques", Proc. SPIE 3125, Propagation and Imaging through the Atmosphere, (23 September 1997); https://doi.org/10.1117/12.279024

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