论文标题
事件概率面具（EPM）和事件DeNoising卷积神经网络（EDNCNN）用于神经形态摄像机
Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras
论文作者
论文摘要
本文提出了一种新颖的方法，用于通过在短时间窗口中计算在每个像素上生成事件的可能性来标记现实世界的神经型相机传感器数据，我们将其称为“事件概率掩码”或EPM。它的应用包括（i）事件DeNoising绩效的客观基准测试，（ii）训练卷积神经网络，以删除噪声，称为“事件DeNoising卷积卷积神经网络”（EDNCNN），以及（iii）估计内部神经形态摄像机参数。我们为现实世界中标有神经形态摄像机事件的第一个数据集（DVSNoise20）提供了噪声。
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.
