A Microparticle Image Velocimetry Based on Light Field Imaging

Accurate 3D flow characterization in a microchannel is becoming increasingly important for the design and development of microfluidic chips. In recent years, a light field camera that can simultaneously record the direction and position information of rays in a single photographic exposure has been...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 19; no. 21; pp. 9806 - 9817
Main Authors: Song, Xianglei, Gu, Mengtao, Cao, Lixia, Tang, Zhiyong, Xu, Chuanlong
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">μ PIV
Abbe imaging principle
Algorithms
Cameras
Chips (electronics)
Chlorophyll
Computer graphics
Computer simulation
Deconvolution
Electrons
Field cameras
Image reconstruction
Lenses
Light
Light field imaging
Lucy–Richardson 3D deconvolution algorithm
Microchannels
Microfluidics
Microparticles
microscale flow
Microscopy
Point spread functions
Three dimensional flow
Three-dimensional displays
three-dimensional velocity vectors
Tracer particles
Velocimetry
Velocity distribution
ISSN:1530-437X, 1558-1748
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Summary:Accurate 3D flow characterization in a microchannel is becoming increasingly important for the design and development of microfluidic chips. In recent years, a light field camera that can simultaneously record the direction and position information of rays in a single photographic exposure has been developed and employed in the field of computer graphics. In this paper, a microparticle image velocimetry based on light field imaging (light field <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>PIV) is proposed to reconstruct the 3D velocity field of a microscale flow. Both simulations and experiments are performed to verify the proposed method. The light field image of tracer particles and the point spread function (PSF) of a light field microscopic imaging system are numerically calculated based on the Abbe imaging principle. The 3D positions of the tracer particles in a flow field are then reconstructed by the Lucy-Richardson 3D deconvolution algorithm. Furthermore, a light field <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>PIV system based on an assembled cage light field camera with a microscope is developed, and calibrations are performed to obtain the geometric parameters of the <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>PIV system accurately. The simulation and experimental results demonstrate the feasibility of the proposed light field <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>PIV. Compared with the synthetic refocusing reconstruction method, the Lucy-Richardson 3D deconvolution algorithm greatly improves the lateral and the axial resolutions of the flow field.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2927414