Denoising for Neuromorphic Cameras Based on Graph Spectral Features

TL;DR

This paper introduces a graph spectral feature-based denoising method for event-based neuromorphic cameras, improving noise removal efficiency and computational speed.

Contribution

The paper presents a novel denoising approach utilizing customized graph Laplacian eigenvectors tailored for event-based camera data.

Findings

Effective noise removal demonstrated on synthetic and real-world data

Reduced computational complexity through eigenvalue reordering

Outperforms alternative denoising methods in experiments

Abstract

Neuromorphic cameras, also known as event-based cameras, can detect changes in the environmental brightness asynchronously and independently for each pixel. They output the brightness changes, i.e., events, as 3-D (2-D pixel coordinates + time) streaming data. While event-based cameras are used in many applications because of their desirable characteristics, e.g., high temporal resolution, low latency, low power consumption, and high dynamic range, their measurements contain considerable noise due to their high sensitivity. In this paper, we propose a denoising method for event-based cameras based on graph spectral features. In the proposed method, we first construct a graph where nodes represent events and edges represent the spatiotemporal distance between the events. To calculate the graph-specified parameter that controls the connectivities of a constructed graph, we utilize the prior on the density of 3-D events. We then calculate the eigenvectors of the graph Laplacian. The obtained eigenvectors are used to extract noiseless events directly. In the calculation of the eigenvectors, we customize the graph Laplacian to reorder its eigenvalues. This allows us to leverage fast eigensolver algorithms instead of the naive eigendecomposition and thereby reduce computational complexity. In experiments on synthetic and real-world event data, we demonstrate that the proposed method effectively removes noise events from the raw events compared to alternative methods.