Interpolation-Based Event Visual Data Filtering Algorithms

TL;DR

This paper introduces four interpolation-based algorithms for filtering noise from event camera data, achieving around 99% noise removal while maintaining signal integrity, suitable for embedded systems.

Contribution

The paper presents four novel IIR filter-based algorithms for noise reduction in event camera data, optimized for low memory usage and real-time embedded applications.

Findings

Achieved approximately 99% noise removal in tests.

Algorithms require about 30KB memory for 1280x720 resolution.

Performed well on both synthetic and real noise datasets.

Abstract

The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99\% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 x 720 and is therefore well suited for implementation in embedded devices.