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Differentiable model-based adaptive optics with transmitted and reflected light.

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    This study combines model-based adaptive optics with machine learning to correct optical aberrations efficiently. This approach requires fewer measurements and works even without a predefined aberration model, improving imaging in biological tissues.

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    Area of Science:

    • Optical imaging
    • Machine learning
    • Adaptive optics

    Background:

    • Optical aberrations limit imaging quality, especially in biological tissues.
    • Machine learning can learn inverse models to correct aberrations but requires extensive datasets.
    • Existing methods may not leverage prior knowledge of the imaging process or handle strongly scattering samples effectively.

    Purpose of the Study:

    • To develop a method combining model-based adaptive optics and machine learning for efficient aberration correction.
    • To enable aberration correction with a minimal number of measurements.
    • To overcome limitations of purely data-driven machine learning approaches and predetermined aberration models.

    Main Methods:

    • Integrating model-based adaptive optics with machine learning optimization techniques.
    • Applying the method in both transmission (single aberrating layer) and reflection (two aberrating layers) configurations.
    • Utilizing optimization that is not restricted to predefined aberration models like Zernike modes.

    Main Results:

    • Successful aberration corrections were achieved using a small number of measurements.
    • The method demonstrated effectiveness in both transmission and reflection imaging setups.
    • Aberration correction was achieved without relying on a specific model of the aberrations themselves.
    • Transmission focusing was successfully demonstrated using only reflected light, compatible with epidetection.

    Conclusions:

    • Combining model-based adaptive optics and machine learning offers a powerful approach for aberration correction in optical imaging.
    • This hybrid method significantly reduces the measurement requirements compared to purely data-driven machine learning.
    • The technique provides flexibility by not being limited by predefined aberration models and enables novel imaging configurations.