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

Abstract

Shack-Hartmann wavefront sensor (SHWFS) is the most popular wavefront sensor in adaptive optics systems. The Deep-Phase-Retrieval Wavefront Reconstruction (DPRWR) method, which was proposed by our group previously, is a kind of deep learning-based wavefront reconstruction method. It can extract more information from the SHWFS images to accurately obtain more Zernike mode coefficients. However, the application limits, performance upper bound, and noise immunity have not been investigated in detail in previous reports. In this paper, subaperture spot sampling, bit depth, number of reconstructed mode coefficients, and noise intensities are analyzed by simulations and experiments to investigate the influence of changes in these parameters on the performance of DPRWR. This work aims to optimize the configuration of DPRWR for better measurement accuracy, spatial resolution, and robustness.