Event-based vision sensors (EVSs), with the core advantages of low latency, high dynamic range, and low data volume, have become one of the research hotspots in the field of computer vision. However, the characteristic of detecting changes in light intensity makes EVSs particularly sensitive to noise, so the large number of noise events in the event stream significantly limits the practical application of EVSs. To address this critical issue, considering the types and characteristics of noise, this paper proposes an event stream denoising algorithm based on local spatiotemporal event quantities and implements it in hardware. To comprehensively evaluate the algorithm’s performance, two metrics based on the probability of real events, Noise Event Ratio (NER) and Event Noise Ratio (ENR), are used to quantify the denoising effect, while hardware resource overhead is assessed in terms of event processing latency and memory usage. Experimental results show that the proposed algorithm achieves an NER of 8.37% and an ENR of 25.10%. Compared to existing denoising algorithms, such as the DWF algorithm, the NER and ENR of this algorithm are reduced by 27.72% and 22.89%, respectively, demonstrating superior denoising performance. On the hardware side, the latency for processing a single event is approximately 110 ns, with a total resource usage of N2 memory units. Although the hardware consumption is slightly higher, the algorithm exhibits significant advantages in denoising performance, providing effective support for the engineering application of EVSs.
Gong et al. (2026) studied this question.