OAH-Net: a deep neural network for efficient and robust hologram reconstruction for off-axis digital holographic microscopy.

ABSTRACT: Off-axis digital holographic microscopy is a high-throughput, label-free imaging technology that provides three-dimensional, high-resolution information about samples, which is 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 s

SUBMITTER: Liu W 

PROVIDER: S-EPMC11919354 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature