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Related Experiment Video

Updated: Jan 8, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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BVSR-EvD: Blurry Video Space-Time Super-Resolution With Events via Diffusion Models.

Wenming Weng, Yueyi Zhang, Wenhao Xu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 17, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces BVSR-EvD, a novel method using event cameras and diffusion models for blurry video restoration. It significantly enhances video super-resolution, improving both spatial and temporal details.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Video restoration from low-resolution, low-frame-rate, and blurry sources is difficult due to limited data.
    • Existing methods struggle with insufficient data priors for effective restoration.

    Purpose of the Study:

    • To propose BVSR-EvD, a novel approach for blurry video space-time super-resolution.
    • To leverage event cameras and diffusion models for enhanced video restoration.

    Main Methods:

    • BVSR-EvD utilizes event-video dual modalities to extract three data priors: motion (events), content (videos), and physical (integration).
    • The Trident Diffusion Model (Trident-DM) decomposes denoising into trident decoupling and adaptive self-composition stages.
    • Meta-networks extract priors, and learned weight maps dynamically integrate them.

    Main Results:

    • BVSR-EvD achieves up to \times 8 spatial super-resolution and \times 64 temporal super-resolution.
    • The method demonstrates superior performance compared to existing techniques on public video datasets.
    • The integrated priors enhance temporal stability, content preservation, and detail.

    Conclusions:

    • BVSR-EvD effectively addresses the challenges of blurry video restoration.
    • The proposed method offers significant improvements in both spatial and temporal super-resolution.
    • This work highlights the potential of combining event cameras and diffusion models for advanced video processing.