Holographic 3D Particle Imaging With Model-Based Deep Network

Gabor holography is an amazingly simple and effective approach for three-dimensional (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 three-dimensional particle imaging. The free-space point spread function (PSF), which is essential for hologram reconstruction, is used as a prior in the MB-HoloNet. All parameters are learned in an end-to-end fashion. The physical prior makes the network efficient and stable for both localization and 3D particle size reconstructions.

Automated summary

In this paper, a model-based holographic network (MB-HoloNet) is proposed for three-dimensional particle imaging, using the free-space point spread function (PSF) as a prior to achieve efficient and stable network performance for localization and 3D particle size reconstructions.