STCC-Filter: A space-time-content correlation-based noise filter with self-adjusting threshold for event camera

Abstract

Bio-inspired event cameras have become a new paradigm of image sensors detecting illumination changes asynchronously and independently for each pixel. However, their sensitivity to noise degrades the output quality. Most existing denoising methods based on spatiotemporal correlation deteriorate in low light conditions due to frequently bursting noise. To tackle this challenge and remove noise for neuromorphic cameras, this paper proposes space–time-content correlation (STCC) and a novel noise filter with self-adjusted threshold, STCC-Filter. In the proposed denoising algorithm, content correlation is modeled based on the brightness change patterns caused by moving objects. Furthermore, space–time and content support from a sequence of events within the range specified by the threshold which can be programmed based on the real application scenarios are fully utilized to improve the robustness and performance of denoising. STCC-Filter is evaluated on widely used datasets and our labeled synthesized datasets. The experimental results demonstrate that the proposed method outperforms traditional spatiotemporal-correlation-based methods in removing more noise and preserving more signals.