Open AccessCentroid computation for Shack-Hartmann wavefront sensor in extreme situations based on artificial neural networks
Author(s) -
Ziqiang Li,
Xinyang Li
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.031675
Subject(s) - centroid , wavefront , adaptive optics , wavefront sensor , robustness (evolution) , artificial neural network , computer science , computation , root mean square , artificial intelligence , optics , least mean squares filter , algorithm , computer vision , physics , adaptive filter , biochemistry , chemistry , quantum mechanics , gene
This paper proposes a method used to calculate centroid for Shack-Hartmann wavefront sensor (SHWFS) in adaptive optics (AO) systems that suffer from strong environmental light and noise pollutions. In these extreme situations, traditional centroid calculation methods are invalid. The proposed method is based on the artificial neural networks that are designed for SHWFS, which is named SHWFS-Neural Network (SHNN). By transforming spot detection problem into a classification problem, SHNNs first find out the spot center, and then calculate centroid. In extreme low signal-noise ratio (SNR) situations with peak SNR (SNR p ) of 3, False Rate of SHNN-50 (SHNN with 50 hidden layer neurons) is 6%, and that of SHNN-900 (SHNN with 900 hidden layer neurons) is 0%, while traditional methods' best result is 26 percent. With the increase of environmental light interference's power, the False Rate of SHNN-900 remains around 0%, while traditional methods' performance decreases dramatically. In addition, experiment results of the wavefront reconstruction are presented. The proposed SHNNs achieve significantly improved performance, compared with the traditional method, the Root Mean Square (RMS) of residual decreases from 0.5349 um to 0.0383 um. This method can improve SHWFS's robustness.