Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks

TL;DR

This paper introduces a compressive Shack-Hartmann wavefront sensing technique enhanced with deep neural networks, enabling accurate wavefront reconstruction using fewer measurements and faster processing, suitable for real-time adaptive optics.

Contribution

The paper proposes a novel compressive sensing approach combined with deep learning to improve wavefront measurement accuracy and speed in Shack-Hartmann sensors under challenging conditions.

Findings

Improved wavefront measurement accuracy in low signal-to-noise scenarios.

Faster wavefront reconstruction enabled by neural network acceleration.

Effective integration with image deconvolution for high-order image restoration.

Abstract

The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this paper, we propose a compressive Shack-Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all sub-apertures, our method reconstructs wavefronts with slope measurements of sub-apertures which have spot images with high signal to noise ratio. Besides, we further propose to use a deep neural network to accelerate wavefront reconstruction speed. During the training stage of the deep neural network, we propose to add a drop-out layer to simulate the compressive sensing process, which could increase development speed of our method. After training, the compressive Shack-Hartmann wavefront sensing method can reconstruct wavefronts in high spatial resolution with slope measurements from only a small amount of sub-apertures. We integrate the straightforward compressive Shack-Hartmann wavefront sensing method with image deconvolution algorithm to develop a high-order image restoration method. We use images restored by the high-order image restoration method to test the performance of our the compressive Shack-Hartmann wavefront sensing method. The results show that our method can improve the accuracy of wavefront measurements and is suitable for real-time applications.