1,460 research outputs found
Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography
Optical diffraction tomography relies on solving an inverse scattering
problem governed by the wave equation. Classical reconstruction algorithms are
based on linear approximations of the forward model (Born or Rytov), which
limits their applicability to thin samples with low refractive-index contrasts.
More recent works have shown the benefit of adopting nonlinear models. They
account for multiple scattering and reflections, improving the quality of
reconstruction. To reduce the complexity and memory requirements of these
methods, we derive an explicit formula for the Jacobian matrix of the nonlinear
Lippmann-Schwinger model which lends itself to an efficient evaluation of the
gradient of the data- fidelity term. This allows us to deploy efficient methods
to solve the corresponding inverse problem subject to sparsity constraints
Optimal Dark Hole Generation via Two Deformable Mirrors with Stroke Minimization
The past decade has seen a significant growth in research targeted at space
based observatories for imaging exo-solar planets. The challenge is in
designing an imaging system for high-contrast. Even with a perfect coronagraph
that modifies the point spread function to achieve high-contrast, wavefront
sensing and control is needed to correct the errors in the optics and generate
a "dark hole". The high-contrast imaging laboratory at Princeton University is
equipped with two Boston Micromachines Kilo-DMs. We review here an algorithm
designed to achieve high-contrast on both sides of the image plane while
minimizing the stroke necessary from each deformable mirror (DM). This
algorithm uses the first DM to correct for amplitude aberrations and the second
DM to create a flat wavefront in the pupil plane. We then show the first
results obtained at Princeton with this correction algorithm, and we
demonstrate a symmetric dark hole in monochromatic light
Robust Estimation and Wavelet Thresholding in Partial Linear Models
This paper is concerned with a semiparametric partially linear regression
model with unknown regression coefficients, an unknown nonparametric function
for the non-linear component, and unobservable Gaussian distributed random
errors. We present a wavelet thresholding based estimation procedure to
estimate the components of the partial linear model by establishing a
connection between an -penalty based wavelet estimator of the
nonparametric component and Huber's M-estimation of a standard linear model
with outliers. Some general results on the large sample properties of the
estimates of both the parametric and the nonparametric part of the model are
established. Simulations and a real example are used to illustrate the general
results and to compare the proposed methodology with other methods available in
the recent literature
Sparsity driven ultrasound imaging
An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data
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