As a neuromorphic sensor with high temporal resolution, the spike camera shows enormous potential in high-speed visual tasks. However, the high-speed sampling of light propagation processes by existing cameras brings unavoidable noise phenomena. Eliminating the unique noise in spike stream is always a key point for spike-based methods. No previous work has addressed the detailed noise mechanism of the spike camera. To this end, we propose a systematic noise model for spike camera based on its unique circuit. In addition, we carefully constructed the noise evaluation equation and experimental scenarios to measure noise variables. Based on our noise model, the first benchmark for spike stream denoising is proposed which includes clear (noisy) spike stream. Further, we design a tailored spike stream denoising framework (DnSS) where denoised spike stream is obtained by decoding inferred inter-spike intervals. Experiments show that DnSS has promising performance on the proposed benchmark. Eventually, DnSS can be generalized well on real spike stream.

翻译：作为一种具有高时间分辨率的神经形态传感器，脉冲相机在高速视觉任务中展现出巨大潜力。然而，现有相机对光传播过程的高速采样不可避免地引入了噪声现象。消除脉冲流中特有的噪声始终是基于脉冲方法的关键问题。此前尚无工作详细研究脉冲相机的噪声机制。为此，我们基于脉冲相机的独特电路提出系统性噪声模型。此外，我们精心构建了噪声评估方程及实验场景以测量噪声变量。基于该噪声模型，我们提出了首个包含清晰（含噪）脉冲流的脉冲流去噪基准。进一步，我们设计了定制的脉冲流去噪框架（DnSS），该框架通过解码推断的脉冲间隔获得去噪脉冲流。实验表明，DnSS在所提基准上取得了优异性能。最终，DnSS能够很好地泛化至真实脉冲流。