无引导星自适应光学与非对称孔径
Guidestar-Free Adaptive Optics with Asymmetric Apertures
AI总结：
本文提出一种无需引导星或波前传感器的闭环自适应光学系统，利用非对称孔径和机器学习实现实时波前校正，通过自然场景测量估计PSF并重建相位畸变，实验验证其在密集自然场景中优于现有方法。
摘要：
本文介绍了一种首个闭环自适应光学（AO）系统，能够在无引导星或波前传感器的情况下实时光学校正像差。近40年前，Cederquist等人证明非对称孔径使相位检索（PR）算法能够实现完全计算波前传感，但计算成本较高。最近，Chimitt等人扩展了这一方法，利用机器学习仅通过单个（基于引导星）点扩散函数（PSF）测量实现了实时波前传感。受这些工作启发，我们提出了一种无引导星的AO框架，结合非对称孔径和机器学习。我们的方法包含三个关键要素：（1）置于系统瞳孔平面的非对称孔径，使PR算法能够实现波前传感；（2）一对机器学习算法，从自然场景测量中估计PSF并重建相位畸变；（3）空间光调制器进行光学校正。我们在未知遮挡物通过的密集自然场景中实验验证了该框架。我们的方法优于现有无引导星波前成型方法，使用数量级更少的测量和计算量。
英文摘要：
This work introduces the first closed-loop adaptive optics (AO) system capable of optically correcting aberrations in real-time without a guidestar or a wavefront sensor. Nearly 40 years ago, Cederquist et al. demonstrated that asymmetric apertures enable phase retrieval (PR) algorithms to perform fully computational wavefront sensing, albeit at a high computational cost. More recently, Chimitt et al. extended this approach with machine learning and demonstrated real-time wavefront sensing using only a single (guidestar-based) point-spread-function (PSF) measurement. Inspired by these works, we introduce a guidestar-free AO framework built around asymmetric apertures and machine learning. Our approach combines three key elements: (1) an asymmetric aperture placed at the system's pupil plane that enables PR-based wavefront sensing, (2) a pair of machine learning algorithms that estimate the PSF from natural scene measurements and reconstruct phase aberrations, and (3) a spatial light modulator that performs optical correction. We experimentally validate this framework on dense natural scenes imaged through unknown obscurants. Our method outperforms state-of-the-art guidestar-free wavefront shaping methods, using an order of magnitude fewer measurements and three orders of magnitude less computation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
