Intelligent metasurfaces: control, communication and computing
Controlling electromagnetic waves and information simultaneously by information
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …
Artificial intelligence-enabled quantitative phase imaging methods for life sciences
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …
label-free investigation of the physiology and pathology of biological systems. This review …
Machine learning and applications in ultrafast photonics
Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …
where machine-learning algorithms are being matched to optical systems to add new …
Deep learning techniques for inverse problems in imaging
Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …
wide variety of inverse problems arising in computational imaging. We explore the central …
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 …
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
In this paper, we employ the emerging paradigm of physics-informed neural networks
(PINNs) for the solution of representative inverse scattering problems in photonic …
(PINNs) for the solution of representative inverse scattering problems in photonic …
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 …
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 …
Roadmap on wavefront shaping and deep imaging in complex media
The last decade has seen the development of a wide set of tools, such as wavefront
shaping, computational or fundamental methods, that allow us to understand and control …
shaping, computational or fundamental methods, that allow us to understand and control …
All-optical machine learning using diffractive deep neural networks
Deep learning has been transforming our ability to execute advanced inference tasks using
computers. Here we introduce a physical mechanism to perform machine learning by …
computers. Here we introduce a physical mechanism to perform machine learning by …