[HTML][HTML] Deep holography

G Situ - Light: Advanced Manufacturing, 2022‏ - light-am.com
With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …

Deep learning for digital holography: a review

T Zeng, Y Zhu, EY Lam - Optics express, 2021‏ - opg.optica.org
Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …

One-step robust deep learning phase unwrap**

K Wang, Y Li, Q Kemao, J Di, J Zhao - Optics express, 2019‏ - opg.optica.org
Phase unwrap** is an important but challenging issue in phase measurement. Even with
the research efforts of a few decades, unfortunately, the problem remains not well solved …

Three-dimensional holographic communication system for the metaverse

L He, K Liu, Z He, L Cao - Optics Communications, 2023‏ - Elsevier
We demonstrate a real-time three-dimensional communication system integrating the
capture, hologram generation, transmission and display. The point cloud of a 3D scene is …

Y-Net: a one-to-two deep learning framework for digital holographic reconstruction

K Wang, J Dou, Q Kemao, J Di, J Zhao - Optics letters, 2019‏ - opg.optica.org
In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic
reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting …

Deep-learning computational holography: A review

T Shimobaba, D Blinder, T Birnbaum, I Hoshi… - Frontiers in …, 2022‏ - frontiersin.org
Deep learning has been develo** rapidly, and many holographic applications have been
investigated using deep learning. They have shown that deep learning can outperform …

DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging

J Wu, L Cao, G Barbastathis - Optics Letters, 2020‏ - opg.optica.org
In mask-based lensless imaging, iterative reconstruction methods based on the geometric
optics model produce artifacts and are computationally expensive. We present a prototype of …

Dense-U-net: dense encoder–decoder network for holographic imaging of 3D particle fields

Y Wu, J Wu, S **, L Cao, G ** - Optics Communications, 2021‏ - Elsevier
Digital holographic imaging is able to reconstruct phase and three-dimensional (3D)
information of an object from a one-shot two-dimensional (2D) lensless hologram. A dense …

Machine learning holography for 3D particle field imaging

S Shao, K Mallery, SS Kumar, J Hong - Optics Express, 2020‏ - opg.optica.org
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 …

Machine learning for flow field measurements: a perspective

S Discetti, Y Liu - Measurement Science and Technology, 2022‏ - iopscience.iop.org
Advancements in machine-learning (ML) techniques are driving a paradigm shift in image
processing. Flow diagnostics with optical techniques is not an exception. Considering the …