Guidestar-Free Adaptive Optics with Asymmetric Apertures

2026-02-02

AI Summary

This work proposes a novel adaptive optics system to address the challenge of optical aberration correction in the absence of guide stars and wavefront sensors. By integrating asymmetric-aperture phase retrieval, machine learning–based point spread function estimation, and phase reconstruction, the method enables closed-loop correction using a spatial light modulator. It achieves, for the first time, real-time aberration correction under completely guide-star-free conditions, requiring only natural scene images to effectively compensate for aberrations induced by unknown obstructions. Compared to existing guide-star-free approaches, the proposed technique reduces the number of required measurements by an order of magnitude and decreases computational complexity by three orders of magnitude, substantially enhancing both efficiency and practicality.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Non-convex Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 in the optical path 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.

Problem

• adaptive optics
• guidestar-free
• wavefront sensing
• asymmetric apertures
• phase retrieval

Innovation

• guidestar-free adaptive optics
• asymmetric apertures
• phase retrieval
• machine learning
• wavefront sensing