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

Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras