41,147 research outputs found
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
In this paper, we present a deep learning-based (DL-based) algorithm, as a
purely mathematical platform, for providing intuitive understanding of the
properties of electromagnetic (EM) wave-matter interaction in nanostructures.
This approach is based on using the dimensionality reduction (DR) technique to
significantly reduce the dimensionality of a generic EM wave-matter interaction
problem without imposing significant error. Such an approach implicitly
provides useful information about the role of different features (or design
parameters such as geometry) of the nanostructure in its response
functionality. To demonstrate the practical capabilities of this DL-based
technique, we apply it to a reconfigurable optical metadevice enabling
dual-band and triple-band optical absorption in the telecommunication window.
Combination of the proposed approach with existing commercialized full-wave
simulation tools offers a powerful toolkit to extract basic mechanisms of
wave-matter interaction in complex EM devices and facilitate the design and
optimization of nanostructures for a large range of applications including
imaging, spectroscopy, and signal processing. It is worth to mention that the
demonstrated approach is general and can be used in a large range of problems
as long as enough training data can be provided
Deep learning versus -minimization for compressed sensing photoacoustic tomography
We investigate compressed sensing (CS) techniques for reducing the number of
measurements in photoacoustic tomography (PAT). High resolution imaging from CS
data requires particular image reconstruction algorithms. The most established
reconstruction techniques for that purpose use sparsity and
-minimization. Recently, deep learning appeared as a new paradigm for
CS and other inverse problems. In this paper, we compare a recently invented
joint -minimization algorithm with two deep learning methods, namely a
residual network and an approximate nullspace network. We present numerical
results showing that all developed techniques perform well for deterministic
sparse measurements as well as for random Bernoulli measurements. For the
deterministic sampling, deep learning shows more accurate results, whereas for
Bernoulli measurements the -minimization algorithm performs best.
Comparing the implemented deep learning approaches, we show that the nullspace
network uniformly outperforms the residual network in terms of the mean squared
error (MSE).Comment: This work has been presented at the Joint Photoacoustics Session with
the 2018 IEEE International Ultrasonics Symposium Kobe, October 22-25, 201
Inverse scattering for reflection intensity phase microscopy
Reflection phase imaging provides label-free, high-resolution characterization of biological samples, typically using interferometric-based techniques. Here, we investigate reflection phase microscopy from intensity-only measurements under diverse illumination. We evaluate the forward and inverse scattering model based on the first Born approximation for imaging scattering objects above a glass slide. Under this design, the measured field combines linear forward-scattering and height-dependent nonlinear back-scattering from the object that complicates object phase recovery. Using only the forward-scattering, we derive a linear inverse scattering model and evaluate this model's validity range in simulation and experiment using a standard reflection microscope modified with a programmable light source. Our method provides enhanced contrast of thin, weakly scattering samples that complement transmission techniques. This model provides a promising development for creating simplified intensity-based reflection quantitative phase imaging systems easily adoptable for biological research.https://arxiv.org/abs/1912.07709Accepted manuscrip
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