Denoising Method for Dynamic Vision Sensor Based on Two-Dimensional Event Density.

The Dynamic Vision Sensor (DVS) is a new type of bionic vision image sensor that offers the advantages of low latency, low power consumption, and high dynamics range compared to conventional sensors. However, background activity (BA) noise will degrade the quality of the DVS output data and lead to unnecessary bandwidth overhead. In dark environments, pixel arrays generate abundant noise, and conventional spatiotemporal filters can hardly achieve satisfactory results. To solve this problem, we exploit the difference in event density distribution between the actual event and noise and propose a denoising method that utilizes the event densities with two neighbors of different radii. Compared to spatiotemporal filters, our approach reduces the error rate on synthetic datasets by at least 35%. Meanwhile, our approach is subjectively more visually appealing. With our denoising method, the performance of DVS can be better in dark conditions.