MSF: Multi-Level Spatiotemporal Filtering for Event Denoising via Motion Estimation

TL;DR

This paper introduces MSF, a new framework that filters noise in event camera data using motion estimation to improve perception accuracy.

Contribution

MSF introduces a novel multi-level spatiotemporal filtering method with motion-compensated aggregation and noise suppression techniques.

Findings

MSF improves the Event Structural Ratio (ESR) on four public benchmarks.

The framework outperforms existing methods under diverse motion and low-light conditions.

Hierarchical event selection enhances reliability by combining spatial, temporal, directional, and polarity consistency.

Abstract

Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, enabling robust perception under fast motion and challenging lighting conditions. Nevertheless, event streams are susceptible to background activity, thermal noise, and hot pixels. Their sparse and irregular patterns can corrupt event structures and degrade downstream tasks. We propose MSF, a multi-level spatiotemporal filtering framework that couples motion-compensated aggregation with neighborhood-level verification. In each temporal window, MSF estimates a constant 2D optical flow by maximizing a robust, density-normalized contrast objective on the image of warped events (IWE). We further incorporate polarity–gradient decorrelation to suppress mixed-polarity noise and an explicit peak-suppression regularizer to avoid hot-pixel-induced degeneracy. The motion parameters are optimized via coarse grid initialization followed by gradient-ascent refinement. Based on the estimated motion, MSF performs hierarchical event selection: central events are extracted from high-confidence aggregated regions, local events are recovered through joint spatial–temporal–directional–polarity consistency, and weak border events are identified using a density-normalized probabilistic support model that rewards support from reliable structures while penalizing self-clustering. Experiments on four public benchmarks (DVSNOISE20, DVSMOTION20, DVSCLEAN, and E-MLB) show that MSF consistently improves the Event Structural Ratio (ESR) and outperforms representative baselines across diverse motion regimes and severe low-light noise.

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