# Digital holographic particle volume reconstruction using a deep neural network

arXiv: https://arxiv.org/abs/1810.09444 · HTML: https://mlsanity.com/paper/1810.09444/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Neural network processing of holographic images](https://mlsanity.com/paper/2203.08898/) (2022)
- [Mimicking non-ideal instrument behavior for hologram processing using neural style translation](https://mlsanity.com/paper/2301.02757/) (2023)
- [FLASHμ: Fast Localizing And Sizing of Holographic Microparticles](https://mlsanity.com/paper/2503.11538/) (2025)

## Abstract (arXiv)

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.