Guidestar-Free Adaptive Optics with Asymmetric Apertures

• URL: http://arxiv.org/abs/2602.07029v1
• Date: Mon, 02 Feb 2026 22:52:42 GMT
• Title: Guidestar-Free Adaptive Optics with Asymmetric Apertures
• Authors: Weiyun Jiang, Haiyun Guo, Christopher A. Metzler, Ashok Veeraraghavan,
• Abstract summary: 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.<n>Inspired by these works, we introduce a guidestar-free AO framework built around asymmetric apertures and machine learning.
• Score: 28.526322406353852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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