This paper presents a novel method for labeling real-world neuromorphic
camera sensor data by calculating the likelihood of generating an event at each
pixel within a short time window, which we refer to as "event probability mask"
or EPM. Its applications include (i) objective benchmarking of event denoising
performance, (ii) training convolutional neural networks for noise removal
called "event denoising convolutional neural network" (EDnCNN), and (iii)
estimating internal neuromorphic camera parameters. We provide the first
dataset (DVSNOISE20) of real-world labeled neuromorphic camera events for noise
removal.

本文提出了一种利用事件概率掩码 (EPM) 来进行真实世界神经形态相机传感器数据标记的新方法，其应用包括基于目标的事件去噪性能基准测试，用于去噪的卷积神经网络的训练（称为事件去噪卷积神经网络 (EDnCNN)），以及估计内部神经形态相机参数。该方法提供了第一个真实世界标记的神经形态相机事件去噪数据集 (DVSNOISE20)。