Convolutional neural networks for object-agnostic wavefront sensing in the presence of noise

Kedar R. Naik; Raymond H. Wright; David D. Claveau; D. Scott Acton; J. Scott Knight

doi:10.1117/12.2576147

13 December 2020 Convolutional neural networks for object-agnostic wavefront sensing in the presence of noise

Kedar R. Naik, Raymond H. Wright, David D. Claveau, D. Scott Acton, J. Scott Knight

Proceedings Volume 11448, Adaptive Optics Systems VII; 114481H (2020) https://doi.org/10.1117/12.2576147
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Abstract

This paper shows that a simple convolutional neural network (CNN) can be used to build an object-agnostic wavefront sensor. Using the well-known Phase Diversity approach as a point of departure, Fourier-space metrics are computed from the conventional and diversity images and then fed to the CNN, which predicts values of the underlying Zernike coefficients. The methodology is shown to work in the presence of Gaussian noise. Prediction errors for defocus, astigmatism, and spherical are on the order of 1/100 of the wavelength.

Conference Presentation

Citation Download Citation

Kedar R. Naik, Raymond H. Wright, David D. Claveau, D. Scott Acton, and J. Scott Knight "Convolutional neural networks for object-agnostic wavefront sensing in the presence of noise", Proc. SPIE 11448, Adaptive Optics Systems VII, 114481H (13 December 2020); https://doi.org/10.1117/12.2576147

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