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.

Keywords

• Centroid prediction
• deep neural network
• Shack-Hartmann sensors
• missing sub-spots
• low signal-to-noise ratio