Neural network for modal compensation of atmospheric turbulence

Peter Wintoft; Guang-Ming Dai

doi:10.1117/12.188065

30 September 1994 Neural network for modal compensation of atmospheric turbulence

Peter Wintoft, Guang-Ming Dai

Proceedings Volume 2302, Image Reconstruction and Restoration; (1994) https://doi.org/10.1117/12.188065
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

In modal compensation of atmospheric turbulence using Zernike polynomials, aliasing has been found to be serious for large sub- aperture configurations. In order to reduce the influence of aliasing on the residual error after modal correction, we have trained a neural network (NN) using simulated array images from a modified Hartmann- Shack wavefront sensor. The array images are derived from simulated atmospheric wavefronts following Kolmogorov turbulence. We find that Zernike coefficients predicted by the NN are more accurate than conventional methods. Using the first 28 Zernike modes, the residual error after modal-NN correction is nearly halved compared to what is obtained with a least-squares solution. In addition, the computation time using the NN is well suitable for real-time application.

Citation Download Citation

Peter Wintoft and Guang-Ming Dai "Neural network for modal compensation of atmospheric turbulence", Proc. SPIE 2302, Image Reconstruction and Restoration, (30 September 1994); https://doi.org/10.1117/12.188065

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