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Reconstruction of the High Resolution Phase in a Closed Loop Adaptive Optics

机译:闭环自适应光学中的高分辨率阶段重建

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Adaptive optics is a commonly used technique to correct the phase distortions caused by the Earth's atmosphere to improve the image quality of the ground-based imaging systems. However, the observed images still suffer from the blur caused by the adaptive optics residual wavefront. In this paper, we propose a model for reconstructing the residual phase in high resolution from a sequence of deformable mirror data. Our model is based on the turbulence statistics and the Taylor frozen flow hypothesis with knowledge of the wind velocities in atmospheric turbulence layers. A tomography problem for the phase distortions from different altitudes is solved in order to get a high quality phase reconstruction. We also consider inexact tomography operators resulting from the uncertainty in the wind velocities. The wind velocities are estimated from the deformable mirror data and, additionally, by including them as unknowns in the objective function. We provide a theoretical analysis on the existence of a minimizer of the objective function. To solve the associated joint optimization problem, we use an alternating minimization method which results in a high resolution reconstruction algorithm with adaptive wind velocities. Numerical simulations are carried out to show the effectiveness of our approach.
机译:自适应光学器件是一种常用的技术,可以纠正地球大气层引起的相变,以提高地面成像系统的图像质量。然而,观察到的图像仍然遭受由自适应光学剩余波前引起的模糊。在本文中,我们提出了一种用于从一系列可变形镜数据中重建高分辨率的残留相的模型。我们的模型基于湍流统计和泰勒冻结的假设,了解大气湍流层中的风速知识。解决了来自不同高度的阶段畸变的断层扫描问题,以获得高质量的相位重建。我们还考虑不确定的断层摄影运算符,这是由于风速的不确定性。通过可变形镜数据估计风速,并且另外,通过将它们作为目标函数中的未知数估计。我们提供了对目标函数最小化器的存在的理论分析。为了解决相关的联合优化问题,我们使用具有自适应风速的高分辨率重建算法的交替最小化方法。进行了数值模拟以显示我们方法的有效性。

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