Denoising for Dynamic Vision Sensor Based on Augmented Spatiotemporal Correlation

Abstract

To effectively denoise the output events of Dynamic Vision Sensor (DVS) in real-time, a denoising method based on augmented spatiotemporal correlation is proposed. In addition, this paper also presents a novel method to evaluate the performance of event denoising. By classifying the events and noise based on their spatiotemporal properties, we use the dynamic response characteristics of DVS under different cases to augment the spatiotemporal correlation, and construct two event filters to process different kinds of events. To benchmark event denoising, we exploit the low-latency of DVS to temporally synchronize raw event stream through motion compensation and obtain the ideal response probability of DVS to quantify the plausibility of events and measure the denoising accuracy. Experiments in various scenes validate that the denoising method is effective at reducing noise with high accuracy and little computational cost, which significantly outperforms the state-of-the-art methods. Moreover, the evaluation method can quantify the denoising accuracy and benchmark different denoising methods objectively and comprehensively.