E-MLB: Multilevel Benchmark for Event-Based Camera Denoising

by   Saizhe Ding, et al.

Event cameras, such as dynamic vision sensors (DVS), are biologically inspired vision sensors that have advanced over conventional cameras in high dynamic range, low latency and low power consumption, showing great application potential in many fields. Event cameras are more sensitive to junction leakage current and photocurrent as they output differential signals, losing the smoothing function of the integral imaging process in the RGB camera. The logarithmic conversion further amplifies noise, especially in low-contrast conditions. Recently, researchers proposed a series of datasets and evaluation metrics but limitations remain: 1) the existing datasets are small in scale and insufficient in noise diversity, which cannot reflect the authentic working environments of event cameras; and 2) the existing denoising evaluation metrics are mostly referenced evaluation metrics, relying on APS information or manual annotation. To address the above issues, we construct a large-scale event denoising dataset (multilevel benchmark for event denoising, E-MLB) for the first time, which consists of 100 scenes, each with four noise levels, that is 12 times larger than the largest existing denoising dataset. We also propose the first nonreference event denoising metric, the event structural ratio (ESR), which measures the structural intensity of given events. ESR is inspired by the contrast metric, but is independent of the number of events and projection direction. Based on the proposed benchmark and ESR, we evaluate the most representative denoising algorithms, including classic and SOTA, and provide denoising baselines under various scenes and noise levels. The corresponding results and codes are available at https://github.com/KugaMaxx/cuke-emlb.


page 1

page 2

page 4

page 7

page 9

page 10

page 11


Event-based, 6-DOF Camera Tracking from Photometric Depth Maps

Event cameras are bio-inspired vision sensors that output pixel-level br...

Within-Camera Multilayer Perceptron DVS Denoising

In-camera event denoising reduces the data rate of event cameras by filt...

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

The main streams of human activity recognition (HAR) algorithms are deve...

An Asynchronous Kalman Filter for Hybrid Event Cameras

We present an Asynchronous Kalman Filter (AKF) to reconstruct High Dynam...

Event-based Camera Pose Tracking using a Generative Event Model

Event-based vision sensors mimic the operation of biological retina and ...

A Framework for Event-based Computer Vision on a Mobile Device

We present the first publicly available Android framework to stream data...

Fast-moving object counting with an event camera

This paper proposes the use of an event camera as a component of a visio...

Please sign up or login with your details

Forgot password? Click here to reset