用脉冲神经网络提升事件相机抗噪能力，让低延迟视觉更稳定。
Spike-TBR: a Noise Resilient Neuromorphic Event Representation
- 将时序二值表示与脉冲神经元结合，实现噪声过滤。
- 四种变体在含噪数据上表现更优，清洁数据也提升。
- 适合需要高鲁棒性的实时事件视觉应用。
事件相机相比传统帧图像传感器具有更高的时间分辨率、更低的延迟和动态范围优势。然而，将事件流高效转换为兼容标准计算机视觉流程的格式仍具挑战性，尤其在存在噪声的情况下。本文提出Spike-TBR，一种基于时序二值表示（TBR）的新颖事件编码策略，通过引入脉冲神经元增强其抗噪能力。Spike-TBR融合了TBR的帧式优势与脉冲神经网络的噪声过滤特性，构建出更鲁棒的事件流表示。我们在多个数据集上评估了四种使用不同脉冲神经元的Spike-TBR变体，结果表明其在含噪场景下性能显著优于基准方法，同时在干净数据上也获得提升。该方法弥合了脉冲处理与帧处理之间的差距，为事件驱动视觉应用提供了一种简洁且抗噪的解决方案。
原文摘要 · Abstract (English)
Event cameras offer significant advantages over traditional frame-based sensors, including higher temporal resolution, lower latency and dynamic range. However, efficiently converting event streams into formats compatible with standard computer vision pipelines remains a challenging problem, particularly in the presence of noise. In this paper, we propose Spike-TBR, a novel event-based encoding strategy based on Temporal Binary Representation (TBR), addressing its vulnerability to noise by integrating spiking neurons. Spike-TBR combines the frame-based advantages of TBR with the noise-filtering capabilities of spiking neural networks, creating a more robust representation of event streams. We evaluate four variants of Spike-TBR, each using different spiking neurons, across multiple datasets, demonstrating superior performance in noise-affected scenarios while improving the results on clean data. Our method bridges the gap between spike-based and frame-based processing, offering a simple noise-resilient solution for event-driven vision applications.
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