A Novel Dynamic Vision Sensor Image Rain Removal Method Based on Event Density Clustering

In recent years, Dynamic Vision Sensors (DVS) have garnered significant attention within the research community, primarily due to their rapid response times, adaptive perceptual capabilities, and notably low power consumption. However, the inherent frameless nature of DVS poses a challenge, rendering traditional camera-based rain removal methodologies inapplicable. This paper introduces an innovative event density-based clustering approach tailored for rain removal in DVS environments. The proposed method entails segmenting the neighborhood surrounding DVS output events into multiple regions, followed by the computation of event density within each region. These computed densities serve as pivotal features for clustering purposes. Notably, the class exhibiting the minimal variance and mean of event density is delineated as the rain class. This classification strategy enables the method to adeptly accommodate diverse hardware configurations and scene variations, thereby facilitating efficient rain information removal. To further enhance the methodology, a novel Skeleton Extraction Density Segmentation (SEDS) technique is proposed to calculate event density. The efficacy of the approach is substantiated through applying the Gaussian Mixture Model (GMM) clustering method, which is systematically compared against the K-means clustering method. This comparative analysis provides a comprehensive quantitative evaluation of clustering performance across an array of images.