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.

本文针对 spike camera 的独特噪声机制，提出了系统的噪声模型，并提出了 spike stream 去噪的基准和 DnSS 框架，实验结果表明 DnSS 在去噪方面表现出色。