Denoising of Event-Based Sensors with Spatial-Temporal Correlation

Abstract

As a novel asynchronous-driven cameras, event-based sensors are with high sensitivity, fast speed, low power consumption and low data volume, but with abundant noise. Since the output of event-based sensors is in the form of address-event-representation (AER), the traditional frame-based denoising method cannot be used. In this paper, we introduce a novel event stream denoising method for such sensors. Effective events tend to show temporal and spatial regularity, while noise events show a kind of randomness. Thus, we build a probabilistic undirected graph model to describe this difference, with which the denoising problem is converted to a probability maximization problem. Then, the model is decomposed into the product of the energy function on the maximum cliques, and the iterated condition model (ICM) is used for energy minimization to obtain the denoised event stream. Experiments show that our method can effectively remove noise events directly from the event stream and significantly improve event recognition rate.