Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) sensor signal. The dynamic nature of the signal and the absence in practical application of the ground truth, it clearly makes difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. In this letter, a new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA). The proposed technique can be used to address an existing DVS issues, which is how to quantitatively characterised noise and signal without ground truth, and how to derive an optimal denoising filter parameters. The solution of the latter problem is demonstrated for the popular real moving-car dataset.

翻译：动态视觉传感器(DVS)因存在大量与原始(纯净)传感器信号混合的背景活动(BA)噪声而具有显著特征。信号的动态特性以及实际应用中缺乏真实标注，使得使用标准图像处理技术难以区分噪声与纯净传感器信号。本文提出了一种基于去趋势波动分析(DFA)来表征BA噪声的新技术。该技术可用于解决现有DVS的两大问题：如何在无真实标注的情况下定量表征噪声与信号，以及如何推导最优去噪滤波参数。后一问题的解决方案已在流行的真实汽车运动数据集上进行了验证。