Centroid-Predicted Deep Neural Network in Shack-Hartmann Sensors

Abstract

The Shack-Hartmann wavefront sensor produces incorrect wavefront measurements when some sub-spots are weak and missing. In this paper, a method is proposed to predict the centroids of these sub-spots for the Shack-Hartmann wavefront sensor based on the deep neural network. Using the centroid information of present sub-spots, the method is able to predict the absent sub-spots' positions. The feasibility and effectiveness of this method are verified by a large number of numerical simulations. The method is applied to wavefront measurement of light with non-uniform near-field intensity. The simulation results show that the proposed method is of great help to improve the measurement accuracy and the Strehl ratio of the focal spot. For wavefronts outside of the training sample, the proposed method shows good generalization and adaptability. In addition, the experiment results demonstrate that the proposed method can predict the missing sub-spots' centroid displacements accurately even though a large proportion of sub-spot is lost randomly.

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Performance Characterization of Deep-Phase-Retrieval Shack-Hartmann Wavefront Sensors

“…> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < These methods are capable to reduce the wavefront measurement error compared to the slope-based method, yet the network is difficult to converge because of large slope measurement errors under strong turbulence. To cope with strong turbulence or strong noise, some researchers have proposed methods to compute the spot center-of-mass position or classify and identify sub-aperture spots with the aid of neural networks [21][22][23].…”