A temporally adaptive schlieren approach by fusing frame- and event-based cameras

This Letter reports what we believe to be a novel schlieren approach with adaptive temporal resolution. The fundamental concept of this approach is to fuse an event-based camera and a low-speed frame-based camera to generate high-frame-rate videos by leveraging the strengths of both. Using a novel experimental setup, events and frames are accurately aligned in both space and time. The aligned data are then fed into a neural network to generate intermediate frames. To examine the proposed approach, tests are conducted on a plate heater, burning candle, and pulsed jet. It has been tested that this approach enables continuous visualization and recording of flows with an adaptive frame rate of up to 3.3 kFPS, breaking through the short operating times of the existing schlieren technique. The developed intermediate frame generation method also outperforms similar methods by minimizing the impact of the event camera readout latency. It achieves a maximum improvement of 3.99 dB in the peak signal-to-noise ratio.

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Institutions

Publication Details

Journal
Optics Letters
Published
2024-12-06
DOI
https://doi.org/10.1364/ol.545691
Citations
6
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.25

Funders

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article

A temporally adaptive schlieren approach by fusing frame- and event-based cameras

Benlong Wang, Zhen Lyu, Yingzheng Liu, Weiwei Cai
6 citations
Optics Letters
Advanced Memory and Neural Computing
1.25
article

A temporally adaptive schlieren approach by fusing frame- and event-based cameras

Benlong Wang, Zhen Lyu, Yingzheng Liu, Weiwei Cai
article en
6 citations

Abstract

This Letter reports what we believe to be a novel schlieren approach with adaptive temporal resolution. The fundamental concept of this approach is to fuse an event-based camera and a low-speed frame-based camera to generate high-frame-rate videos by leveraging the strengths of both. Using a novel experimental setup, events and frames are accurately aligned in both space and time. The aligned data are then fed into a neural network to generate intermediate frames. To examine the proposed approach, tests are conducted on a plate heater, burning candle, and pulsed jet. It has been tested that this approach enables continuous visualization and recording of flows with an adaptive frame rate of up to 3.3 kFPS, breaking through the short operating times of the existing schlieren technique. The developed intermediate frame generation method also outperforms similar methods by minimizing the impact of the event camera readout latency. It achieves a maximum improvement of 3.99 dB in the peak signal-to-noise ratio.

Optics LettersVol. 50(2)
Shanghai Jiao Tong University (CN)
National Natural Science Foundation of China, National Postdoctoral Program for Innovative Talents
Openalex Percentile: Top 19%
Advanced Memory and Neural Computing
1.25
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