Liu, W., Delikoyun, K., Chen, Q. et al. OAH-Net: A Deep Neural Network for Hologram Reconstruction of Off-axis Digital Holographic Microscope. arXiv:2410.13592v1 [physics.optics] (2024).
This research paper introduces OAH-Net, a deep learning model designed to overcome the bottleneck of computationally intensive hologram reconstruction in off-axis digital holographic microscopy (DHM), aiming to achieve real-time analysis for high-throughput applications, particularly in clinical diagnostics.
The researchers developed OAH-Net, a deep neural network architecture that integrates Fourier Imager Heads (FIHs) and a Complex Valued Network (CVN). FIHs filter undesired components and correct frequency shifts in the hologram's frequency domain, while CVN converts the object beam wave into amplitude and unwrapped phase images. The model was trained in a weakly supervised manner using a large dataset of blood cell samples, with target images generated using Ovizio API software. The performance of OAH-Net was evaluated based on reconstruction accuracy, inference speed, and generalization ability on unseen samples with distinct patterns. Additionally, the model's utility in downstream tasks was assessed by integrating it with object detection models (YOLOv5 and YOLOv8) for blood cell analysis.
OAH-Net presents a significant advancement in hologram reconstruction for off-axis DHM, offering a fast, accurate, and generalizable solution that addresses the limitations of existing methods. Its ability to perform real-time reconstruction without compromising accuracy in downstream tasks makes it a valuable tool for high-throughput biological and medical imaging applications.
This research significantly contributes to the field of quantitative phase imaging by providing an efficient and reliable deep learning-based solution for hologram reconstruction. The development of OAH-Net has the potential to accelerate the adoption of DHM in various biomedical applications, particularly in clinical diagnostics where real-time analysis is crucial.
While OAH-Net demonstrates robust performance, its generalization ability might be limited to samples with phase values within the training data range. Future research could explore techniques to further enhance the model's robustness to a wider range of phase values and investigate its applicability in other imaging modalities beyond DHM.
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by Wei Liu, Ker... kl. arxiv.org 10-18-2024
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