Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imaging sensors. However, they are sensitive to background activity (BA) events that are unwanted. There are some filters for tackling this problem based on spatio-temporal correlation. However, they are either memory-intensive or computing-intensive. We propose \emph{SeqXFilter}, a spatio-temporal correlation filter with only a past event window that has an O(1) space complexity and has simple computations. We explore the spatial correlation of an event with its past few events by analyzing the distribution of the events when applying different functions on the spatial distances. We find the best function to check the spatio-temporal correlation for an event for \emph{SeqXFilter}, best separating real events and noise events. We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's performance. Four neuromorphic event-based datasets, recorded from four DVS with different output sizes, are used for validation of our method. The experimental results show that \emph{SeqXFilter} achieves similar performance as baseline NNb filters, but with extremely small memory cost and simple computation logic.

中文翻译：

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SeqXFilter：用于动态视觉传感器的内存高效降噪滤波器

与基于帧的成像传感器相比，基于神经形态事件的动态视觉传感器 (DVS) 具有更快的采样率和更高的动态范围。但是，它们对不需要的背景活动 (BA) 事件很敏感。有一些过滤器可以基于时空相关性来解决这个问题。但是，它们要么是内存密集型的，要么是计算密集型的。我们提出了\emph{SeqXFilter}，这是一个时空相关滤波器，它只有一个过去的事件窗口，它的空间复杂度为 O(1) 并且计算简单。我们通过在空间距离上应用不同函数时分析事件的分布来探索事件与其过去几个事件的空间相关性。我们找到了最好的函数来检查 \emph{SeqXFilter} 的事件的时空相关性，最好分离真实事件和噪声事件。我们不仅给出了滤波器的视觉去噪效果，还使用了两个指标来定量分析滤波器的性能。从具有不同输出大小的四个 DVS 记录的四个基于神经形态事件的数据集用于验证我们的方法。实验结果表明，\emph{SeqXFilter} 实现了与基线 NNb 滤波器相似的性能，但具有极小的内存成本和简单的计算逻辑。