提出四类滤波算法，可去99%噪声并保留有效信号
Interpolation-Based Event Visual Data Filtering Algorithms
- 基于IIR滤波矩阵设计四类事件数据滤波算法
- 在多个数据集上实现约99%噪声去除率
- 仅需30KB内存，适合嵌入式设备部署
神经形态视觉领域快速发展，事件相机正被广泛应用于各类场景。然而，传感器输出的数据流存在显著噪声。本文提出一种事件数据处理方法，可在保留大部分有效信号的同时，去除约99%的噪声。基于无限脉冲响应（IIR）滤波矩阵，设计了四种算法，并在多个事件数据集上进行了对比测试，这些数据集通过人工加噪或动态视觉传感器真实记录的噪声进行修改。所提方法在1280×720分辨率传感器下仅需约30KB内存，因此非常适合在嵌入式设备中实现。
原文摘要 · Abstract (English)
The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99\% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 x 720 and is therefore well suited for implementation in embedded devices.
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