[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 …
Deep-learning-based fringe-pattern analysis with uncertainty estimation
Deep learning has gained increasing attention in the field of optical metrology and
demonstrated great potential in solving a variety of optical metrology tasks, such as fringe …
demonstrated great potential in solving a variety of optical metrology tasks, such as fringe …
Generalized framework for non-sinusoidal fringe analysis using deep learning
Phase retrieval from fringe images is essential to many optical metrology applications. In the
field of fringe projection profilometry, the phase is often obtained with systematic errors if the …
field of fringe projection profilometry, the phase is often obtained with systematic errors if the …
Fringe pattern analysis using deep learning
In many optical metrology techniques, fringe pattern analysis is the central algorithm for
recovering the underlying phase distribution from the recorded fringe patterns. Despite …
recovering the underlying phase distribution from the recorded fringe patterns. Despite …
Deep neural networks for single shot structured light profilometry
S Van der Jeught, JJJ Dirckx - Optics express, 2019 - opg.optica.org
In 3D optical metrology, single-shot structured light profilometry techniques have inherent
advantages over their multi-shot counterparts in terms of measurement speed, optical setup …
advantages over their multi-shot counterparts in terms of measurement speed, optical setup …
Does deep learning always outperform simple linear regression in optical imaging?
Deep learning has been extensively applied in many optical imaging problems in recent
years. Despite the success, the limitations and drawbacks of deep learning in optical …
years. Despite the success, the limitations and drawbacks of deep learning in optical …
Deep learning for optical sensor applications: A review
Over the past decade, deep learning (DL) has been applied in a large number of optical
sensors applications. DL algorithms can improve the accuracy and reduce the noise level in …
sensors applications. DL algorithms can improve the accuracy and reduce the noise level in …
At the intersection of optics and deep learning: statistical inference, computing, and inverse design
Deep learning has been revolutionizing information processing in many fields of science
and engineering owing to the massively growing amounts of data and the advances in deep …
and engineering owing to the massively growing amounts of data and the advances in deep …
Rapid and robust two-dimensional phase unwrapping via deep learning
Two-dimensional phase unwrapping algorithms are widely used in optical metrology and
measurements. The high noise from interference measurements, however, often leads to the …
measurements. The high noise from interference measurements, however, often leads to the …
Phase unwrapping in optical metrology via denoised and convolutional segmentation networks
The interferometry technique is commonly used to obtain the phase information of an object
in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove …
in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove …
Related searches
- deep learning optical metrology
- phase unwrapping optical metrology
- segmentation networks optical metrology
- deep learning phase recovery
- deep learning fringe analysis
- deep learning sensor applications
- deep learning inverse design
- deep learning statistical inference
- phase unwrapping segmentation networks
- deep learning optical imaging
- deep learning intersection of optics