Machine learning holography for 3D particle field imaging
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 22, 2020
- Source ID
- 10.1364/oe.379480
Entities
People
- Jiarong Hong
- Kevin Mallery
- S. Santosh Kumar
- Siyao Shao
Organizations
- Office of Naval Research