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Resolving phase ambiguity via physically optimized single-image sensorless wavefront sensing with neural networks.

Ying Zhang, Zhiqiang Wang, Haojin Li

    Optics Express
    |March 18, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Deep learning wavefront sensing offers a simple, efficient, and low-cost adaptive optics solution. A new method, POSS, resolves phase ambiguity in single images, achieving high accuracy comparable to dual-image methods.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Image Processing

    Background:

    • Deep learning wavefront sensing is a promising adaptive optics technique due to its simplicity, efficiency, and low cost.
    • Phase ambiguity, where distinct phase aberrations produce identical image patterns, limits single-image deep learning performance.

    Purpose of the Study:

    • To develop a physically optimized single-image sensorless (POSS) method for accurate wavefront reconstruction.
    • To address and resolve the phase ambiguity inherent in single-image wavefront sensing.

    Main Methods:

    • Integration of a lightweight ResNet-18 model with a physics-guided sign correction strategy.
    • Implementation of progressive sign correction strategies to balance accuracy and computational cost.

    Main Results:

    • POSS effectively resolves sign ambiguity for even-order Zernike coefficients.
    • Achieved reconstruction accuracy comparable to high-precision dual-image phase diversity methods across a wide turbulence range (D/r0 = 1-15).
    • Demonstrated millisecond-level inference, stable residual RMS under varying noise, and improved high-frequency recovery.

    Conclusions:

    • POSS provides accurate, robust, and real-time wavefront reconstruction from a single in-focus image.
    • Offers a low-complexity, hardware-friendly solution for intelligent adaptive optics systems.