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    Area of Science:

    • Optical Engineering
    • Machine Learning Applications
    • Image Processing

    Background:

    • Traditional wavefront sensing often involves complex optical setups and computationally intensive image analysis.
    • Developing simplified and efficient wavefront sensing techniques is crucial for various optical applications.

    Purpose of the Study:

    • To introduce a new class of wavefront sensors leveraging machine learning to simplify hardware and processing.
    • To demonstrate the capability of deep learning models in directly estimating wavefront aberrations from single intensity images.

    Main Methods:

    • Utilized a convolutional neural network (CNN) for image-based wavefront sensing.
    • Experimentally validated diverse wavefront sensing architectures.
    • Trained the deep learning model to estimate Zernike coefficients from single intensity images.

    Main Results:

    • Successfully demonstrated image-based wavefront sensing architectures using a CNN.
    • Showcased direct estimation of Zernike coefficients from single intensity images.
    • Validated the deep learning wavefront sensor's ability to handle aberrations from point and extended sources.

    Conclusions:

    • Machine learning, specifically deep learning, offers a powerful approach to simplify wavefront sensor design and operation.
    • The proposed method enables efficient and accurate wavefront aberration estimation, reducing hardware complexity.
    • This technique holds promise for advancing optical metrology and adaptive optics systems.