Particle Image Velocimetry Algorithm Based on Spike Camera Adaptive Integration

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.