Deep learning wavefront sensing and aberration correction in atmospheric turbulence
Kaiqiang Wang, Mengmeng Zhang, Ju Tang, Lingke Wang, Liusen Hu, Xiaoyan Wu, 李威 Li Wei, Jianglei Di, Guodong Liu, Jianlin Zhao
Abstract
Abstract Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways: (i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What’s more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.