# Machine Learning Holography for 3D Particle Field Imaging

arXiv: https://arxiv.org/abs/1911.00805 · HTML: https://mlsanity.com/paper/1911.00805/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [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)
- [On the joint estimation of flow fields and particle properties from Lagrangian data](https://mlsanity.com/paper/2510.00479/) (2025)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly-dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.