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High-speed Particle Image Velocimetry Near Surfaces
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Particle Image Velocimetry Algorithm Based on Spike Camera Adaptive Integration.

Xiaoqiang Li1,2,3, Changxu Wu4, Yichao Wang1

  • 1School of Mechanics and Engineering Sciences, Peking University, Beijing 100871, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel particle image velocimetry (PIV) algorithm using neuromorphic vision sensors (NVS) to overcome overexposure issues in high-illumination environments. The spike camera-based PIV method significantly improves particle detection and velocity field accuracy, outperforming traditional cameras in challenging conditions.

Keywords:
adaptive integrationhigh speed cameraneuromorphic vision sensoroverexposureparticle image velocimetryspike camera

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Area of Science:

  • Fluid dynamics
  • Optical measurement techniques
  • Biomimetic sensors

Background:

  • Overexposure in Particle Image Velocimetry (PIV) reduces image quality and velocity estimation accuracy, especially at liquid-gas interfaces.
  • Traditional frame-based cameras struggle with high illumination, leading to pixel saturation and particle detection failure.
  • Accurate velocity field measurements are crucial in various fluid dynamics applications.

Purpose of the Study:

  • To address the challenge of overexposure in PIV caused by high illumination.
  • To develop a PIV algorithm capable of effective particle detection in overexposed regions.
  • To enable accurate velocity field measurements in challenging environments like liquid-gas interfaces.

Main Methods:

  • Proposed a PIV algorithm utilizing adaptive integral spike camera data from a neuromorphic vision sensor (NVS).
  • Implemented target-background segmentation on high-frequency digital spike signals to suppress high illumination.
  • Adaptively integrated spike data based on illumination and particle velocity features to reconstruct high signal-to-noise ratio (SNR) images.

Main Results:

  • Simulations showed the spike-based camera had 8.594 times less average flow velocity estimation error in overexposed areas compared to frame-based cameras.
  • Experimental results demonstrated successful capture of continuous high-density particle trajectories.
  • Measurable and continuous velocity fields were obtained even in the presence of high illumination challenges.

Conclusions:

  • The proposed PIV algorithm effectively mitigates overexposure issues caused by high illumination, particularly at liquid-gas interfaces.
  • Neuromorphic vision sensors offer a viable solution for PIV in previously challenging measurement scenarios.
  • The spike camera-based approach significantly enhances particle detection and velocity field accuracy in high-illumination conditions.