Event Denoising Based on Iterative Tree-Structured Information Aggregation

Event cameras play a crucial role in the visual field; however, they are susceptible to noise. Traditional denoising algorithms for event cameras often struggle to balance accuracy and speed. To address this issue, this paper proposes an algorithm named Event Denoising Based on Iterative Tree-Structured Information Aggregation (EDIST). Specifically, the proposed method first establishes connections in event streams using a spatiotemporal window to extract Relation Tree. Then, a pruning algorithm is employed to streamline the subsequent information aggregation process, followed by a multi-stage convolution module designed to process the Relation Tree and obtain aggregated features. Finally, these aggregated features are fed into a classification module to determine whether an event is noise. During the inference stage, an information storage reuse module is designed to enable iterative execution, thereby enhancing inference speed. Experimental results demonstrate that the proposed algorithm outperforms existing methods in both denoising accuracy and inference speed on the DVSNOISE20 dataset.