A motion denoising algorithm with Gaussian self-adjusting threshold for event camera

Event cameras, characterized by their low power consumption, expansive dynamic range, and high temporal resolution, have attracted great attentions in various computer vision tasks. Compared to frame-based cameras, event cameras exemplify a marked paradigmatic transition in data formation and output. However, the quality of event streams is compromised by background activity and hot pixels, leading to increased computational overheads and sub-optimal outcomes in subsequent applications, notably in recognition, video reconstruction, and target detection tasks. In this paper, a two-step denoising algorithm (referred as GMCM) is proposed to counteract these challenges. The GMCM algorithm comprises two steps: Gaussian denoising preprocessing and motion denoising. The former incorporates Gaussian temporal distribution and adaptive thresholding mechanisms to discern the inclusion of motion-related information within the event streams. Experimental results demonstrate that Gaussian denoising preprocessing can not only adeptly discern whether the event data stream contains motion information but also enhance computational efficiency. Conclusively, the GMCM algorithm achieves state-of-the-art performance, yielding SNR scores of 37.22 and 26.79 on the DVSCLEAN dataset at the noise ratios of 50% and 100%, respectively.