Guidestar-Free Adaptive Optics with Asymmetric Apertures
For imaging systems requiring adaptive optics, this work eliminates the need for guidestars, enabling simpler and more robust real-time aberration correction in natural scenes.
This paper presents the first closed-loop adaptive optics system that corrects aberrations in real-time without a guidestar or wavefront sensor, using asymmetric apertures and machine learning. It outperforms existing guidestar-free methods with an order of magnitude fewer measurements and three orders of magnitude less computation.
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.