无需导星和波前传感器，用不对称光圈+机器学习实现实时自适应光学校正。
Guidestar-Free Adaptive Optics with Asymmetric Apertures
- 通过不对称光圈实现计算波前感知，结合机器学习从自然场景中估计点扩散函数。
- 仅需少量测量（少一个数量级）和极少计算量（少三个数量级），性能超越现有方法。
- 适合在无导星、复杂遮挡环境下进行实时成像校正的科研与工程人员。
本文首次提出无需导星或波前传感器的闭环自适应光学（AO）系统，可在真实场景中实时校正像差。近40年前，Cederquist等人证明了不对称光圈可使相位重构（PR）算法实现全计算波前感知，但计算成本高。近期Chimitt等人利用机器学习，仅凭单个导星点扩散函数（PSF）实现实时波前感知。受此启发，我们提出一种基于不对称光圈与机器学习的无导星AO框架。该方法包含三要素：(1) 置于系统瞳面的不对称光圈，支持基于PR的波前感知；(2) 一对机器学习模型，从自然场景测量中估计PSF并重建相位像差；(3) 空间光调制器实现光学校正。我们在未知遮蔽物下对密集自然场景成像进行了实验验证，结果表明，本方法在测量次数上比现有最优技术减少一个数量级，计算量降低三个数量级，同时性能更优。
原文摘要 · Abstract (English)
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.
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