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

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

Keywords: digital holographic microscopy, deep learning, holographic reconstruction and phase retrieval

TL;DR: OAH-Net enables real-time, quantitative and highly accurate blood cell analysis by rapidly reconstructing off-axis holograms using physics-guided neural network for point of care diagnostics.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1364/BOE.547292

Submission Number: 80