High-throughput terahertz imaging: progress and challenges
Many exciting terahertz imaging applications, such as non-destructive evaluation,
biomedical diagnosis, and security screening, have been historically limited in practical …
biomedical diagnosis, and security screening, have been historically limited in practical …
Review of diffractive deep neural networks
In 2018, a UCLA research group published an important paper on optical neural network
(ONN) research in the journal Science. It developed the world's first all-optical diffraction …
(ONN) research in the journal Science. It developed the world's first all-optical diffraction …
All-optical image classification through unknown random diffusers using a single-pixel diffractive network
Classification of an object behind a random and unknown scattering medium sets a
challenging task for computational imaging and machine vision fields. Recent deep learning …
challenging task for computational imaging and machine vision fields. Recent deep learning …
Integration of programmable diffraction with digital neural networks
Optical imaging and sensing systems based on diffractive elements have seen massive
advances over the last several decades. Earlier generations of diffractive optical processors …
advances over the last several decades. Earlier generations of diffractive optical processors …
Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
Quantitative phase imaging (QPI) is a label-free computational imaging technique used in
various fields, including biology and medical research. Modern QPI systems typically rely on …
various fields, including biology and medical research. Modern QPI systems typically rely on …
[HTML][HTML] Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
Free-space optical information transfer through diffusive media is critical in many
applications, such as biomedical devices and optical communication, but remains …
applications, such as biomedical devices and optical communication, but remains …
Numerical simulations on optoelectronic deep neural network hardware based on self-referential holography
We propose a novel optoelectronic deep neural network (OE-DNN) hardware called the self-
referential holographic deep neural network (SR-HDNN). The SR-HDNN features a …
referential holographic deep neural network (SR-HDNN). The SR-HDNN features a …
Terahertz deep-optics imaging enabled by perfect lens-initialized optical and electronic neural networks
Terahertz imaging has shown great potential in various fields and applications, such as non-
destructive testing, security screening and biomedical researches. However, many factors …
destructive testing, security screening and biomedical researches. However, many factors …
Two-photon nanolithography of sub-micrometer thickness microlenses designed by NPCC assisted Rayleigh Sommerfeld diffraction integral
Recent development of artificial neural networks (ANNs) and inverse design methods have
demonstrated their prospective significance for planar diffractive lens design, with a plethora …
demonstrated their prospective significance for planar diffractive lens design, with a plethora …
BlurryScope: a cost-effective and compact scanning microscope for automated HER2 scoring using deep learning on blurry image data
We developed a rapid scanning optical microscope, termed" BlurryScope", that leverages
continuous image acquisition and deep learning to provide a cost-effective and compact …
continuous image acquisition and deep learning to provide a cost-effective and compact …