Improved Machine Learning Approach For Wavefront Sensing

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/r(0) = 20, the detection root-mean-square of wavefront error of the proposed method is 0.1307 lambda, which has a better accuracy than existing neural networks. When D/r(0) = 15 and 10, the root-mean-square error is respectively 0.0909 lambda and 0.0718 lambda. It has certain applicative value in the case of medium and weak turbulence. The root-mean-square error of experiment results with D/r(0) = 20 is 0.1304 lambda, proving the correctness of simulations. Moreover, this method only needs 12 ms to accomplish the calculation and it has broad prospects for real-time wavefront sensing.