Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras
R. Wes Baldwin, Mohammed Almatrafi, Vijayan K. Asari, Keigo Hirakawa
Abstract
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.
Topics & Concepts
Neuromorphic engineeringConvolutional neural networkComputer scienceEvent (particle physics)Noise reductionArtificial intelligenceBenchmarkingNoise (video)PixelComputer visionArtificial neural networkPattern recognition (psychology)Image (mathematics)PhysicsQuantum mechanicsBusinessMarketingAdvanced Memory and Neural ComputingCCD and CMOS Imaging SensorsNeural dynamics and brain function