Computational 4D imaging

Digital inline holography is an amazingly simple and effective approach for three-dimensional 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 firstly, from which fluid flows are computed. Without iterative reconstructions, This sequential space-time process lacks accuracy. We propose 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.

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Model-based 3D holographic imaging

Gabor holography is a simple and effective approach for 3D imaging. However, it suffers from a DC term, twin-image entanglement, and defocus noise. The conventional approach for solving this problem is either using an off-axis setup, or compressive holography. The former sacrifices simplicity, and the latter is computationally demanding and time consuming. To cope with this problem, we propose a model-based holographic network (MB-HoloNet) for 3D particle imaging. The free-space point spread function is used as a prior in the MB-HoloNet. All parameters are learned end-to-end. The physical prior makes the network efficient and stable for both localization and 3D particle size reconstructions.

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