Optica Publishing Group
Browse

OAH-Net: A Deep Neural Network for Efficient and Robust Hologram Reconstruction for Off-axis Digital Holographic Microscopy

Version 2 2025-02-04, 14:15
Version 1 2025-02-04, 14:15
Posted on 2025-02-04 - 14:15
Off-axis digital holographic microscopy is a high-throughput, label-free imaging technology that provides three-dimensional, high-resolution information about samples, particularly useful in large-scale cellular imaging. However, the hologram reconstruction process poses a significant bottleneck for timely data analysis. To address this challenge, we propose a novel reconstruction approach that integrates deep learning with the physical principles of off-axis holography. We initialized part of the network weights based on the physical principle and then fine-tuned them via supersized learning. Our off-axis hologram network (OAH-Net) retrieves phase and amplitude images with errors that fall within the measurement error range attributable to hardware, and its reconstruction speed significantly surpasses the microscope’s acquisition rate. Crucially, OAH-Net, trained and validated on diluted whole blood samples, demonstrates remarkable external generalization capabilities on unseen samples with distinct patterns. Additionally, it can be seamlessly integrated with other models for downstream tasks, enabling end-to-end real-time hologram analysis. This capability further expands off-axis holography’s applications in both biological and medical studies.

CITE THIS COLLECTION

DataCite
No result found
or
Select your citation style and then place your mouse over the citation text to select it.

SHARE

email

Usage metrics

Biomedical Optics Express

AUTHORS (10)

Wei Liu
Kerem Delikoyun
Qianyu Chen
Alperen Yildiz
Si Ko Myo
Win Sen Kuan
John Tshon Yit Soong
Matthew Edward Cove
Oliver Hayden
Hwee Kuan Lee

CATEGORIES

need help?