A Denoising Algorithm for Event Cameras Based on Low-Dimensional Manifold Constraint

This paper proposes a new denoising algorithm for event cameras based on manifold constraints. An event camera is a new type of vision sensor, which can perceive the change of scene brightness with high temporal resolution (microseconds) and output the event stream with pixel position, time and polarity. Event cameras are disturbed by noise while transmitting brightness changes, and noisy event streams will adversely affect subsequent applications. In order to solve this problem, this paper assumes that events are distributed on a low-dimensional manifold in high-dimensional space, uses the similarity information between event points to build a graph to approximate the manifold structure, and combines the manifold smoothing constraint of graph to complete the event stream denoising. For the first time, the proposed algorithm introduces the graph-based manifold constraint into the event denoising problem and directly processes successive event sequences. Experiments on simulated data and real datasets demonstrate the effectiveness of the event denoising algorithm.