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 …
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 …
General deep learning framework for emissivity engineering
Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve
desired target emissivity spectra, as a typical emissivity engineering, for broad applications …
desired target emissivity spectra, as a typical emissivity engineering, for broad applications …
Snapshot multispectral imaging using a diffractive optical network
Multispectral imaging has been used for numerous applications in eg, environmental
monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical …
monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical …
Universal linear intensity transformations using spatially incoherent diffractive processors
Under spatially coherent light, a diffractive optical network composed of structured surfaces
can be designed to perform any arbitrary complex-valued linear transformation between its …
can be designed to perform any arbitrary complex-valued linear transformation between its …
Unidirectional imaging using deep learning–designed materials
A unidirectional imager would only permit image formation along one direction, from an
input field-of-view (FOV) A to an output FOV B, and in the reverse path, B→ A, the image …
input field-of-view (FOV) A to an output FOV B, and in the reverse path, B→ A, the image …
Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network
Large-scale linear operations are the cornerstone for performing complex computational
tasks. Using optical computing to perform linear transformations offers potential advantages …
tasks. Using optical computing to perform linear transformations offers potential advantages …
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 …
Data‐class‐specific all‐optical transformations and encryption
Diffractive optical networks provide rich opportunities for visual computing tasks. Here, data‐
class‐specific transformations that are all‐optically performed between the input and output …
class‐specific transformations that are all‐optically performed between the input and output …
Unlocking ultra-high holographic information capacity through nonorthogonal polarization multiplexing
J Wang, J Chen, F Yu, R Chen, J Wang, Z Zhao… - Nature …, 2024 - nature.com
Contemporary studies in polarization multiplexing are hindered by the intrinsic orthogonality
constraints of polarization states, which restrict the scope of multiplexing channels and their …
constraints of polarization states, which restrict the scope of multiplexing channels and their …