On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
Phase imaging with an untrained neural network
Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …
a supervised training strategy, and thus need a large training set to optimize their weights …
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Deep learning-based image reconstruction methods have achieved remarkable success in
phase recovery and holographic imaging. However, the generalization of their image …
phase recovery and holographic imaging. However, the generalization of their image …
[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 …
neural networks (DNN) have become tremendously powerful tools to solve many …
One-step robust deep learning phase unwrapping
Phase unwrapping 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 …
Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging
Artificial intelligence (AI) techniques such as deep learning (DL) for computational imaging
usually require to experimentally collect a large set of labeled data to train a neural network …
usually require to experimentally collect a large set of labeled data to train a neural network …
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 …
Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning
Tomographic flow cytometry by digital holography is an emerging imaging modality capable
of collecting multiple views of moving and rotating cells with the aim of recovering their …
of collecting multiple views of moving and rotating cells with the aim of recovering their …
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 …