[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 …
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 …
Quantitative phase imaging based on holography: trends and new perspectives
Abstract In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering
solution to a quantitative description of the optical wavefront. After 75 years of development …
solution to a quantitative description of the optical wavefront. After 75 years of development …
[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 …
Deep learning spatial phase unwrapping: a comparative review
Phase unwrapping is an indispensable step for many optical imaging and metrology
techniques. The rapid development of deep learning has brought ideas to phase …
techniques. The rapid development of deep learning has brought ideas to phase …
PhaseNet 2.0: Phase unwrapping of noisy data based on deep learning approach
GE Spoorthi, RKSS Gorthi… - IEEE transactions on image …, 2020 - ieeexplore.ieee.org
Phase unwrapping is an ill-posed classical problem in many practical applications of
significance such as 3D profiling through fringe projection, synthetic aperture radar and …
significance such as 3D profiling through fringe projection, synthetic aperture radar and …
Deep learning-based branch-cut method for InSAR two-dimensional phase unwrapping
Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many
synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the …
synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the …
Artificial intelligence in interferometric synthetic aperture radar phase unwrapping: A review
L Zhou, H Yu, Y Lan - IEEE Geoscience and Remote Sensing …, 2021 - ieeexplore.ieee.org
Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy
and remote sensing applications, eg, topography reconstruction and subsidence estimation …
and remote sensing applications, eg, topography reconstruction and subsidence estimation …
Deep learning for the detection and phase unwrapping of mining-induced deformation in large-scale interferograms
This article proposes deep convolutional neural networks to detect and map localized, rapid
subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar …
subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar …
Single-shot fringe projection profilometry based on deep learning and computer graphics
Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent
years. However, to obtain a large amount of data from actual systems for training is still a …
years. However, to obtain a large amount of data from actual systems for training is still a …