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Differential Leveling01:12

Differential Leveling

Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...

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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Deep learning-enhanced holographic wavefront sensor for high-order aberration sensing.

Ming Liu, Bing Dong

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    A novel deep learning-enhanced holographic wavefront sensor (DLHWS) improves accuracy and mode detection over traditional methods. This advanced sensor overcomes limitations like speckle noise for better aberration measurement.

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    Area of Science:

    • Optical Engineering
    • Computational Imaging
    • Machine Learning Applications

    Background:

    • Conventional holographic modal wavefront sensors (HMWS) face limitations including measurement inaccuracies due to speckle noise from computer-generated holograms (CGHs).
    • Existing HMWS also have restricted capabilities in measuring a wide range of aberration modes.

    Purpose of the Study:

    • To introduce a deep learning-enhanced holographic wavefront sensor (DLHWS) to overcome the limitations of traditional HMWS.
    • To enhance wavefront sensing accuracy and expand the capability for detecting high-order aberrations.

    Main Methods:

    • Developed DLHWS utilizing deep neural networks to process multiple biased images from a CGH.
    • Implemented two configurations: DLHWS-c using a convolutional neural network (CNN) for modal coefficient estimation and DLHWS-p using a UNet for direct phase map reconstruction.

    Main Results:

    • DLHWS demonstrated significant improvements in wavefront sensing accuracy and the detection of high-order aberrations through simulations and experiments.
    • DLHWS-c achieved superior inference speed and high accuracy for low-order modes.
    • DLHWS-p provided higher precision for high-order aberrations (hundreds of modes) induced by atmospheric turbulence, albeit with greater computational demands.

    Conclusions:

    • The proposed DLHWS effectively addresses the limitations of conventional SMI-HMWS, offering enhanced performance.
    • DLHWS presents a versatile solution for wavefront sensing, with distinct advantages in speed and precision depending on the network architecture and application.