# Guidestar-Free Adaptive Optics with Asymmetric Apertures

arXiv: https://arxiv.org/abs/2602.07029 · HTML: https://mlsanity.com/paper/2602.07029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 68 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Efficient Visual State Space Model for Image Deblurring](https://mlsanity.com/paper/2405.14343/) (2024)
- [Optical Aberration Correction in Postprocessing using Imaging Simulation](https://mlsanity.com/paper/2305.05867/) (2023)
- [Encoding complex fields by using a phase-only optical element: mitigation of pixel crosstalk effects](https://mlsanity.com/paper/1903.06046/) (2019)
- [Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network](https://mlsanity.com/paper/2303.12573/) (2023)
- [Guidestar-free image-guided wavefront-shaping](https://mlsanity.com/paper/2007.03956/) (2020)
- [WaveMo: Learning Wavefront Modulations to See Through Scattering](https://mlsanity.com/paper/2404.07985/) (2024)
- [Wavefront Estimation From a Single Measurement: Uniqueness and Algorithms](https://mlsanity.com/paper/2504.09395/) (2025)
- [Fluorescent wavefront shaping using incoherent iterative phase conjugation](https://mlsanity.com/paper/2205.06215/) (2022)
- [DARTS: Diffusion Approximated Residual Time Sampling for Time-of-flight Rendering in Homogeneous Scattering Media](https://mlsanity.com/paper/2402.03106/) (2024)
- [Fovea Stacking: Imaging with Dynamic Localized Aberration Correction](https://mlsanity.com/paper/2506.00716/) (2025)

## Built on by (most cited first, 2 of 2)

- [High-speed Imaging through Turbulence with Event-based Light Fields](https://mlsanity.com/paper/2603.14023/) (2026)
- [Pupil Design for Computational Wavefront Estimation](https://mlsanity.com/paper/2604.00225/) (2026)

## Abstract (arXiv)

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.