KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/669578
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AbstractDigital inline holography is an amazingly simple and effective approach for 3D imaging, to which particle tracking velocimetry is of particular interest. Conventional digital holographic particle tracking velocimetry techniques are computationally separated in particle and flow reconstruction, plus the expensive computations. Usually, the particle volumes are recovered first, from which fluid flows are computed. Without iterative reconstructions, This sequential space–time process lacks accuracy. This paper presents a joint optimization framework for digital holographic particle tracking velocimetry: particle volumes and fluid flows are reconstructed jointly in a higher space–time dimension, enabling faster convergence and better reconstruction quality of both fluid flow and particle volumes within a few minutes on modern GPUs. Synthetic and experimental results are presented to show the efficiency of the proposed technique.
CitationChen, N., Wang, C., & Heidrich, W. (2021). Snapshot Space–Time Holographic 3D Particle Tracking Velocimetry. Laser & Photonics Reviews, 2100008. doi:10.1002/lpor.202100008
SponsorsN.C. and C.W. contributed equally to this work. The authors thank Jinhui Xiong and Guangming Zang for constructive discussions, Prof. Sigurdur Thoroddsen and Ziqiang Yang from High-Speed Fluids Imaging Laboratory at King Abdullah University of Science and Technology for preparing the particles, and design the flow experiments. This work was supported by the KAUST individual baseline funding.
JournalLaser & Photonics Reviews
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