Digital holographic particle volume reconstruction using a deep neural network

TL;DR

This paper introduces a deep neural network approach for direct particle volume reconstruction from in-line holograms, enabling faster detection of particle positions and sizes with improved efficiency over traditional methods.

Contribution

The paper presents a novel deep neural network method that directly reconstructs particle volumes from holograms, reducing computational time and improving detection accuracy.

Findings

Faster reconstruction compared to traditional diffraction methods

Simultaneous detection of particle positions and sizes

Numerical analysis shows reduced errors in detection

Abstract

This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network. Digital holographic volume reconstruction conventionally uses multiple diffraction calculations to obtain sectional reconstructed images from an in-line hologram, followed by detection of the lateral and axial positions, and the sizes of particles by using focus metrics. However, the axial resolution is limited by the numerical aperture of the optical system, and the processes are time-consuming. The method proposed here can simultaneously detect the lateral and axial positions, and the particle sizes via a deep neural network (DNN). We numerically investigated the performance of the DNN in terms of the errors in the detected positions and sizes. The calculation time is faster than conventional diffracted-based approaches.