154 research outputs found
Spectral pre-modulation of training examples enhances the spatial resolution of the Phase Extraction Neural Network (PhENN)
The Phase Extraction Neural Network (PhENN) is a computational architecture,
based on deep machine learning, for lens-less quantitative phase retrieval from
raw intensity data. PhENN is a deep convolutional neural network trained
through examples consisting of pairs of true phase objects and their
corresponding intensity diffraction patterns; thereafter, given a test raw
intensity pattern PhENN is capable of reconstructing the original phase object
robustly, in many cases even for objects outside the database where the
training examples were drawn from. Here, we show that the spatial frequency
content of the training examples is an important factor limiting PhENN's
spatial frequency response. For example, if the training database is relatively
sparse in high spatial frequencies, as most natural scenes are, PhENN's ability
to resolve fine spatial features in test patterns will be correspondingly
limited. To combat this issue, we propose "flattening" the power spectral
density of the training examples before presenting them to PhENN. For phase
objects following the statistics of natural scenes, we demonstrate
experimentally that the spectral pre-modulation method enhances the spatial
resolution of PhENN by a factor of 2.Comment: 12 pages, 10 figure
Hamiltonian and Phase-Space Representation of Spatial Solitons
We use Hamiltonian ray tracing and phase-space representation to describe the
propagation of a single spatial soliton and soliton collisions in a Kerr
nonlinear medium. Hamiltonian ray tracing is applied using the iterative
nonlinear beam propagation method, which allows taking both wave effects and
Kerr nonlinearity into consideration. Energy evolution within a single spatial
soliton and the exchange of energy when two solitons collide are interpreted
intuitively by ray trajectories and geometrical shearing of the Wigner
distribution functions.Comment: 12 pages, 5 figure
Shift multiplexing with spherical reference waves
Shift multiplexing is a holographic storage method particularly suitable for the implementation of holographic disks. We characterize the performance of shift-multiplexed memories by using a spherical wave as the reference beam. We derive the shift selectivity, the cross talk, the exposure schedule, and the storage density of the method. We give experimental results to verify the theoretical predictions
Volume Holographic Hyperspectral Imaging
A volume hologram has two degenerate Bragg-phase-matching dimensions and provides the capability of volume holographic imaging. We demonstrate two volume holographic imaging architectures and investigate their imaging resolution, aberration, and sensitivity. The first architecture uses the hologram directly as an objective imaging element where strong aberration is observed and confirmed by simulation. The second architecture uses an imaging lens and a transmission geometry hologram to achieve linear two-dimensional optical sectioning and imaging of a four-dimensional (spatial plus spectral dimensions) object hyperspace. Multiplexed holograms can achieve simultaneously three-dimensional imaging of an object without a scanning mechanism
Coherence retrieval using trace regularization
The mutual intensity and its equivalent phase-space representations quantify
an optical field's state of coherence and are important tools in the study of
light propagation and dynamics, but they can only be estimated indirectly from
measurements through a process called coherence retrieval, otherwise known as
phase-space tomography. As practical considerations often rule out the
availability of a complete set of measurements, coherence retrieval is usually
a challenging high-dimensional ill-posed inverse problem. In this paper, we
propose a trace-regularized optimization model for coherence retrieval and a
provably-convergent adaptive accelerated proximal gradient algorithm for
solving the resulting problem. Applying our model and algorithm to both
simulated and experimental data, we demonstrate an improvement in
reconstruction quality over previous models as well as an increase in
convergence speed compared to existing first-order methods.Comment: 28 pages, 10 figures, accepted for publication in SIAM Journal on
Imaging Science
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
We have developed a globally applicable diagnostic Covid-19 model by
augmenting the classical SIR epidemiological model with a neural network
module. Our model does not rely upon previous epidemics like SARS/MERS and all
parameters are optimized via machine learning algorithms employed on publicly
available Covid-19 data. The model decomposes the contributions to the
infection timeseries to analyze and compare the role of quarantine control
policies employed in highly affected regions of Europe, North America, South
America and Asia in controlling the spread of the virus. For all continents
considered, our results show a generally strong correlation between
strengthening of the quarantine controls as learnt by the model and actions
taken by the regions' respective governments. Finally, we have hosted our
quarantine diagnosis results for the top 70 affected countries worldwide, on a
public platform, which can be used for informed decision making by public
health officials and researchers alike.Comment: 21 pages, 16 figure
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