  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.
