425 research outputs found
Parametric Level Set Methods for Inverse Problems
In this paper, a parametric level set method for reconstruction of obstacles
in general inverse problems is considered. General evolution equations for the
reconstruction of unknown obstacles are derived in terms of the underlying
level set parameters. We show that using the appropriate form of parameterizing
the level set function results a significantly lower dimensional problem, which
bypasses many difficulties with traditional level set methods, such as
regularization, re-initialization and use of signed distance function.
Moreover, we show that from a computational point of view, low order
representation of the problem paves the path for easier use of Newton and
quasi-Newton methods. Specifically for the purposes of this paper, we
parameterize the level set function in terms of adaptive compactly supported
radial basis functions, which used in the proposed manner provides flexibility
in presenting a larger class of shapes with fewer terms. Also they provide a
"narrow-banding" advantage which can further reduce the number of active
unknowns at each step of the evolution. The performance of the proposed
approach is examined in three examples of inverse problems, i.e., electrical
resistance tomography, X-ray computed tomography and diffuse optical
tomography
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
We present a framework for the end-to-end optimization of metasurface imaging
systems that reconstruct targets using compressed sensing, a technique for
solving underdetermined imaging problems when the target object exhibits
sparsity (i.e. the object can be described by a small number of non-zero
values, but the positions of these values are unknown). We nest an iterative,
unapproximated compressed sensing reconstruction algorithm into our end-to-end
optimization pipeline, resulting in an interpretable, data-efficient method for
maximally leveraging metaoptics to exploit object sparsity. We apply our
framework to super-resolution imaging and high-resolution depth imaging with a
phase-change material: in both situations, our end-to-end framework
computationally discovers optimal metasurface structures for compressed sensing
recovery, automatically balancing a number of complicated design
considerations. The optimized metasurface imaging systems are robust to noise,
significantly improving over random scattering surfaces and approaching the
ideal compressed sensing performance of a Gaussian matrix, showing how a
physical metasurface system can demonstrably approach the mathematical limits
of compressed sensing
Proceedings of the FEniCS Conference 2017
Proceedings of the FEniCS Conference 2017 that took place 12-14 June 2017 at the University of Luxembourg, Luxembourg
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