Adaptive Event Address Map Denoising for Event Cameras

Abstract

Event cameras provide high temporal resolution and high dynamic range and have application prospects in areas such as visual navigation, robotics, and image reconstruction. Event cameras output an asynchronous stream of address events that can be highly polluted by noise due to lighting and motion conditions. In order to better analyze and process noise with different spatiotemporal correlation, we introduce a method using an event address map (EAM) representation and classify event noise into background noise, near-edge noise, and recessive noise. In addition, since near-edge noise and recessive noise have a closer spatiotemporal correlation with real events, they are difficult to suppress with current denoising methods. Accordingly, we propose an adaptive EAM denoising method by adjusting the time window to form the EAM by measuring the number of events after pre-denoising and performing median filtering and opening. The method can better adapt to scenes of different motions to process near-edge noise, and can generate events to fill recessive noise. The output EAM can be directly used for subsequent process. Alternatively, to provide high temporal resolution, we restore the stream of address events from the output EAM. To suitably evaluate event denoising performance on real-world data, we introduce the average local event variance and amount of recessive noise, and the proposed method achieves improvements of 24.94% and 72.79%, respectively, compared with conventional denoising.