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Related Experiment Video

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Super-Resolving Dynamic Scenes With Spike Camera via Multi-Frame Sequential Alignment With Motion Propagation.

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    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 6, 2025
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    Summary
    This summary is machine-generated.

    Spike cameras reconstruct high-resolution images from temporal data. This study introduces a novel network to overcome challenges like fluctuations and motion blur, improving image quality from spike streams.

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    Area of Science:

    • Neuromorphic Engineering
    • Computer Vision
    • Image Processing

    Background:

    • Spike cameras offer high temporal resolution for dynamic scenes.
    • Reconstructing high-resolution images from spike streams is challenging due to fluctuations and motion.

    Purpose of the Study:

    • To develop a super-resolution network for spike cameras.
    • To address intensity extraction and temporal alignment issues in high-speed scenes.

    Main Methods:

    • A region-adaptive temporal filtering module to mitigate spike fluctuations and extract intensity.
    • A multi-frame feature alignment module using long-term temporal information.
    • Motion information propagation from neighboring moments to aid alignment.

    Main Results:

    • The proposed network effectively mitigates spike fluctuations.
    • Accurate temporal alignment is achieved even with large motions.
    • State-of-the-art performance demonstrated on synthetic and real spike data.

    Conclusions:

    • The developed spike camera super-resolution network enhances image reconstruction.
    • The network successfully addresses key challenges in processing spike stream data.
    • This work advances high-speed dynamic scene imaging using neuromorphic sensors.