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.

中文翻译：

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基于模型的深层网络的全息3D粒子成像

Gabor全息术是用于三维（3D）成像的极其简单有效的方法。但是，它会遭受DC项，双像纠缠和散焦噪声的困扰。解决此问题的常规方法是使用离轴设置或压缩全息照相。前者牺牲了简单性，而后者则在计算上非常费时且费时。为了解决这个问题，我们提出了一种用于三维粒子成像的基于模型的全息网络（MB-HoloNet）。全息图重建必不可少的自由空间点扩展函数（PSF）在MB-HoloNet中被用作先验。所有参数都是以端到端的方式学习的。物理先验使网络对于本地化和3D粒度重建均有效且稳定。