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Non-Uniform Exposure Imaging via Neuromorphic Shutter Control.

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    This study introduces a Neuromorphic Shutter Control (NSC) system using event-based cameras to adaptively manage exposure, reducing motion blur and noise. A self-supervised denoising network (SEID) further enhances image quality in challenging real-world conditions.

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    Area of Science:

    • Computer Vision
    • Neuromorphic Engineering
    • Image Processing

    Background:

    • Conventional cameras struggle with real-time motion perception for adaptive shutter control, limiting imaging in harsh environments.
    • Non-uniform exposures offer flexibility but introduce challenges in managing motion blur and noise.
    • Existing methods lack the ability to perceive intra-frame dynamic information for adaptive exposure control.

    Purpose of the Study:

    • To develop a novel Neuromorphic Shutter Control (NSC) system for real-time adaptive camera shutter control.
    • To overcome limitations of conventional cameras in dynamic environments by leveraging event-based sensing.
    • To enhance image quality by mitigating motion blur and noise through adaptive exposure and advanced denoising.

    Main Methods:

    • Proposed a Neuromorphic Shutter Control (NSC) system utilizing the low latency of event-based sensors for real-time motion monitoring and adaptive exposure.
    • Developed an event-based image denoising network (SEID) using self-supervised learning to stabilize Signal-to-Noise Ratio (SNR) and denoise images.
    • Explored image noise statistics and inter-frame motion information from events to generate self-supervision signals for SEID.
    • Implemented the NSC system in a hybrid-camera prototype and collected a synchronized real-world dataset.

    Main Results:

    • The NSC system effectively avoids motion blur and alleviates instant noise in dynamic scenes.
    • The SEID network successfully stabilizes inconsistent SNR caused by non-uniform exposure times.
    • Experiments on synthetic and real-world datasets demonstrate superior performance compared to state-of-the-art methods.
    • The hybrid-camera prototype validates the system's effectiveness in diverse real-world scenarios.

    Conclusions:

    • The proposed Neuromorphic Shutter Control (NSC) system offers a robust solution for adaptive imaging in challenging environments.
    • Event-based sensing combined with self-supervised learning enables high-quality image acquisition with real-time motion adaptation.
    • The developed system significantly advances the capabilities of adaptive camera shutter control for practical applications.