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    Area of Science:

    • Computational Imaging
    • Photonics and Sensors
    • Computer Vision

    Background:

    • Single-photon avalanche diodes (SPADs) offer picosecond timing resolution for photon detection, crucial for applications like LiDAR and low-light imaging.
    • SPAD cameras capture high-speed binary images, enabling 3D reconstruction of dynamic environments, but face challenges with motion blur and lower spatial resolution compared to passive cameras.

    Purpose of the Study:

    • To develop a novel computational imaging algorithm for improved 3D reconstruction of moving scenes using SPAD data.
    • To address motion blur and enhance the spatial resolution of depth images derived from high-speed SPAD events.
    • To generate non-blurred, high-resolution depth images synchronized with the frame rate of conventional passive cameras.

    Main Methods:

    • A plug-and-play approach within an optimization framework was employed.
    • The algorithm alternates between guided video super-resolution of the 3D scene and precise image realignment using optical flow.
    • The method processes high-speed SPAD events to produce depth images at a lower, synchronized frame rate.

    Main Results:

    • Experiments on synthetic data demonstrated significant improvements in image resolution across various signal-to-noise ratios and photon levels.
    • Validation with real-world SPAD measurements confirmed the method's effectiveness in dynamic scenarios, including fast-moving objects and human subjects.
    • Successful application across different ranges (3m to 325m) and sensor types (consumer-grade to short-wave infrared SPAD cameras) was achieved.

    Conclusions:

    • The proposed computational imaging algorithm effectively mitigates motion blur and enhances spatial resolution in SPAD-based 3D reconstruction.
    • The method proves robust and versatile for reconstructing dynamic scenes under diverse real-world conditions.
    • This advancement holds significant potential for applications requiring high-fidelity 3D information from SPAD sensors in challenging environments.