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Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Deep-learning-based beam position estimation with a photon-counting camera in free-space optical communications.

Xialin Liu, Ying Guo, Jun Huang

    Optics Express
    |December 19, 2025
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    Summary

    A new deep-learning method enhances deep-space optical sensing by accurately estimating beacon beam centers. This deep-learning-based super-resolution beam position estimator (DSRBPE) achieves sub-pixel precision even with noisy, weak signals.

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    Area of Science:

    • Optical sensing and communication
    • Deep learning applications
    • Photon detection

    Background:

    • Photon-counting cameras offer high sensitivity and time resolution for deep-space applications.
    • Accurate estimation of beacon beam centers is crucial but difficult due to photon noise and fluctuations.

    Purpose of the Study:

    • To develop a deep-learning-based super-resolution beam position estimator (DSRBPE).
    • To improve the accuracy of beam position estimation for weak optical signals in deep space.

    Main Methods:

    • Developed a deep-learning model incorporating photon data modeling.
    • Optimized a super-resolution convolutional neural network framework and loss function.
    • Utilized simulation and experimental validation.

    Main Results:

    • Achieved sub-pixel precision of 0.2 pixels for centroid extraction.
    • Successfully estimated the position of a weak 4x4 pixel Gaussian spot on a 32x32 detector.
    • Demonstrated superior robustness under low signal-to-noise ratio conditions compared to conventional algorithms.

    Conclusions:

    • DSRBPE significantly enhances the accuracy of beam position estimation in deep-space optical sensing.
    • The proposed method offers a robust solution for challenging low signal-to-noise environments.
    • Deep learning provides a powerful approach for improving optical sensing capabilities.