A Method Used to Improve the Dynamic Range of Shack–Hartmann Wavefront Sensor in Presence of Large Aberration

Abstract

With the successful application of the Shack–Hartmann wavefront sensor in measuring aberrations of the human eye, researchers found that, when the aberration is large, the local wavefront distortion is large, and it causes the spot corresponding to the sub-aperture of the microlens to shift out of the corresponding range of the sub-aperture. However, the traditional wavefront reconstruction algorithm searches for the spot within the corresponding range of the sub-aperture of the microlens and reconstructs the wavefront according to the calculated centroid, which leads to wavefront reconstruction errors. To solve the problem of the small dynamic range of the Shack–Hartmann wavefront sensor, this paper proposes a wavefront reconstruction algorithm based on the autocorrelation method and a neural network. The autocorrelation centroid extraction method was used to calculate the centroid in the entire spot map in order to obtain a centroid map and to reconstruct the wavefront by matching the centroid with the microlens array through the neural network. This method breaks the limitation of the sub-aperture of the microlens. The experimental results show that the algorithm improves the dynamic range of the first 15 terms of the Zernike aberration reconstruction to varying degrees, ranging from 62.86% to 183.87%.

Reinforcement Learning Environment for Wavefront Sensorless Adaptive Optics in Single-Mode Fiber Coupled Optical Satellite Communications Downlinks

“…The AO method was first proposed by Babcock [38] to improve observational astronomical by correcting wavefronts with a deformable optical element controlled by a wavefront sensor. New techniques and results have since been consistently published, primarily focusing on advancements in wavefront sensors, deformable mirrors, and control algorithms [39][40][41][42][43].…”

Large Range of a High-Precision, Independent, Sub-Mirror Three-Dimensional Co-Phase Error Sensing and Correction Method via a Mask and Population Algorithm

“…In this process, the co-phase errors of each sub-mirror need to be detected first and then corrected by a high-precision displacement adjusting mechanism [ 4 , 5 , 6 ]. At this stage, there are many commonly used wavefront sensing methods, some of which depend on hardware devices [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], while others are based on images [ 15 , 16 , 17 , 18 ].…”

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].…”