[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
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 …
Concept, implementations and applications of Fourier ptychography
The competition between resolution and the imaging field of view is a long-standing problem
in traditional imaging systems—they can produce either an image of a small area with fine …
in traditional imaging systems—they can produce either an image of a small area with fine …
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 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 …
DeepCGH: 3D computer-generated holography using deep learning
The goal of computer-generated holography (CGH) is to synthesize custom illumination
patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative …
patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative …
[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 …
Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning
Simultaneous imaging of various facets of intact biological systems across multiple
spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is …
spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is …
Fourier ptychography: current applications and future promises
Traditional imaging systems exhibit a well-known trade-off between the resolution and the
field of view of their captured images. Typical cameras and microscopes can either “zoom in” …
field of view of their captured images. Typical cameras and microscopes can either “zoom in” …
Review of bio-optical imaging systems with a high space-bandwidth product
Optical imaging has served as a primary method to collect information about biosystems
across scales—from functionalities of tissues to morphological structures of cells and even at …
across scales—from functionalities of tissues to morphological structures of cells and even at …