LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising

TL;DR

This paper introduces LED, a large-scale real-world paired dataset for event camera denoising, and proposes a novel denoising framework leveraging dual events and bio-inspired neurons, demonstrating superior performance.

Contribution

The paper provides a new high-resolution, diverse real-world event denoising dataset and a novel denoising framework utilizing homogeneous dual events and bio-inspired neuron models.

Findings

The proposed framework outperforms existing methods on multiple datasets.

LED dataset contains 3K sequences with diverse noise levels and high-resolution event streams.

The bio-inspired baseline achieves accurate denoising results.

Abstract

Event camera has significant advantages in capturing dynamic scene information while being prone to noise interference, particularly in challenging conditions like low threshold and low illumination. However, most existing research focuses on gentle situations, hindering event camera applications in realistic complex scenarios. To tackle this limitation and advance the field, we construct a new paired real-world event denoising dataset (LED), including 3K sequences with 18K seconds of high-resolution (1200*680) event streams and showing three notable distinctions compared to others: diverse noise levels and scenes, larger-scale with high-resolution, and high-quality GT. Specifically, it contains stepped parameters and varying illumination with diverse scenarios. Moreover, based on the property of noise events inconsistency and signal events consistency, we propose a novel effective denoising framework(DED) using homogeneous dual events to generate the GT with better separating noise from the raw. Furthermore, we design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholds to realize accurate denoising. The experimental results demonstrate that the remarkable performance of the proposed approach on different datasets.The dataset and code are at https://github.com/Yee-Sing/led.