[HTML][HTML] Deep learning in optical metrology: a review
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
[HTML][HTML] 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 …
Towards real-time photorealistic 3D holography with deep neural networks
The ability to present three-dimensional (3D) scenes with continuous depth sensation has a
profound impact on virtual and augmented reality, human–computer interaction, education …
profound impact on virtual and augmented reality, human–computer interaction, education …
[HTML][HTML] 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 …
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Imaging through scattering is an important yet challenging problem. Tremendous progress
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …
[HTML][HTML] 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 …
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 …
[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 …
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 …
[HTML][HTML] Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography
Holography provides access to the optical phase. The emerging compressive phase
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …