A Dynamic Visual Sensor Denoising Method Based on Event Density Classification

Dynamic Visual Sensors (DVS) output pixel information asynchronously as an address event, only transmitting information for pixels where the intensity change exceeds a threshold, thereby reducing redundant data generation at the source. They possess advantages such as low-latency response and high efficiency with low power consumption. However, factors such as thermal noise and leakage current can lead to noise events even when the light intensity remains constant, which affects image quality and subsequent processing. In this paper, we propose a new method for calculating event density called Local Density Segmentation (LSD), which divides events and their surrounding neighborhoods into different regions for independent event density calculation. Based on LSD-calculated event density, we further propose a denoising method named Local Density Segmentation K-means (LSD-K), which utilizes event density as a feature to select and remove noise events. Finally, through experiments, we validate the performance of our algorithm.