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[HTML][HTML] Deep holography
With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …
neural networks (DNN) have become tremendously powerful tools to solve many …
Deep learning for digital holography: a review
Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
One-step robust deep learning phase unwrap**
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 …
the research efforts of a few decades, unfortunately, the problem remains not well solved …
Three-dimensional holographic communication system for the metaverse
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 …
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
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 …
reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting …
Deep-learning computational holography: A review
Deep learning has been develo** rapidly, and many holographic applications have been
investigated using deep learning. They have shown that deep learning can outperform …
investigated using deep learning. They have shown that deep learning can outperform …
DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging
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 …
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
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 …
information of an object from a one-shot two-dimensional (2D) lensless hologram. A dense …
Machine learning holography for 3D particle field imaging
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 …
Our approach uses a U-net architecture incorporating residual connections, Swish …
Machine learning for flow field measurements: a perspective
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 …
processing. Flow diagnostics with optical techniques is not an exception. Considering the …