Sensors, Vol. 19, Pages 3533: Improved Machine Learning Approach for Wavefront Sensing

Sensors, Vol. 19, Pages 3533: Improved Machine Learning Approach for Wavefront Sensing Sensors doi: 10.3390/s19163533 Authors: Hongyang Guo Yangjie Xu Qing Li Shengping Du Dong He Qiang Wang Yongmei Huang In the adaptive optics (AO) system, to improve the effectiveness and accuracy of wavefront sensing-less technology, a phase-based sensing approach using machine learning is proposed. In contrast to the traditional gradient-based optimization methods, the model we designed is based on an improved convolutional neural network. Specifically, the deconvolution layer, which reconstructs unknown input by measuring output, is introduced to represent the phase maps of the point spread functions at the in focus and defocus planes. The improved convolutional neural network is utilized to establish the nonlinear mapping between the input point spread functions and the corresponding phase maps of the optical system. Once well trained, the model can directly output the aberration map of the optical system with good precision. Adequate simulations and experiments are introduced to demonstrate the accuracy and real-time performance of the proposed method. The simulations show that even when atmospheric conditions D/r0 = 20, the detection root-mean-square of wavefront error of the proposed method is 0.1307 λ, which has a better accuracy than existing neural networks. When D/r0 = 15 and 10, the root-mean-square error is respectively 0.0909 λ ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research