Neuromorphic Imaging With Density-Based Spatiotemporal Denoising

Bio-inspired neuromorphic cameras asynchronously record visual information of dynamic scenes by discrete events. Due to the high sampling rate, they are capable of fast motion capture without causing image blur, overcoming the drawbacks of frame-based cameras that produce blurry recordings of dynamic objects. However, highly sensitive neuromorphic cameras are also susceptible to interference, and can generate a lot of noise in response. Such noisy event data can dramatically degrade the event-based observations and analysis. Existing methods have insufficient performance on noise suppression, especially for the weak dynamic scenes where noise resembles signals in attributes and distribution, and their results thus have limited improvements on downstream applications. Such demanding cases have not been fully investigated. We aim to seek a solution with more effective and robust discrimination between the two types of events, such that the denoised output can benefit neuromorphic classification tasks. Therefore, we propose a sub-quadratic clustering algorithm tailored for neuromorphic data. It couples event priors with density estimation for noise removal on raw event streams, where strongly correlated signals are taken to be denser in space-time. Experiments on real and synthetic samples illustrate that our simple and interpretable algorithm can suppress noise significantly, and can show greater accuracy and robustness than other techniques in some challenging scenarios.