# Interpolation-Based Event Visual Data Filtering Algorithms

arXiv: https://arxiv.org/abs/2507.01557 · HTML: https://mlsanity.com/paper/2507.01557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection](https://mlsanity.com/paper/2004.01689/) (2020)
- [Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm](https://mlsanity.com/paper/2207.00860/) (2022)
- [Foveal-pit inspired filtering of DVS spike response](https://mlsanity.com/paper/2105.14331/) (2021)

## Abstract (arXiv)

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.