391 research outputs found
A dissipative time reversal technique for photo-acoustic tomography in a cavity
We consider the inverse source problem arising in thermo- and photo-acoustic
tomography. It consists in reconstructing the initial pressure from the
boundary measurements of the acoustic wave. Our goal is to extend versatile
time reversal techniques to the case of perfectly reflecting boundary of the
domain. Standard time reversal works only if the solution of the direct problem
decays in time, which does not happen in the setup we consider. We thus propose
a novel time reversal technique with a non-standard boundary condition. The
error induced by this time reversal technique satisfies the wave equation with
a dissipative boundary condition and, therefore, decays in time. For larger
measurement times, this method yields a close approximation; for smaller times,
the first approximation can be iteratively refined, resulting in a convergent
Neumann series for the approximation
Disparity and Optical Flow Partitioning Using Extended Potts Priors
This paper addresses the problems of disparity and optical flow partitioning
based on the brightness invariance assumption. We investigate new variational
approaches to these problems with Potts priors and possibly box constraints.
For the optical flow partitioning, our model includes vector-valued data and an
adapted Potts regularizer. Using the notation of asymptotically level stable
functions we prove the existence of global minimizers of our functionals. We
propose a modified alternating direction method of minimizers. This iterative
algorithm requires the computation of global minimizers of classical univariate
Potts problems which can be done efficiently by dynamic programming. We prove
that the algorithm converges both for the constrained and unconstrained
problems. Numerical examples demonstrate the very good performance of our
partitioning method
Reconstructions for some coupled-physics inverse problems
This letter announces and summarizes results obtained in arXiv:1111.5051 and
considers several natural extensions. The aforementioned paper proposes a
procedure to reconstruct coefficients in a second-order, scalar, elliptic
equation from knowledge of a sufficiently large number of its solutions. We
present this derivation and extend it to show which parameters may or may not
be reconstructed for several hybrid (also called coupled physics) imaging
modalities including photo-acoustic tomography, thermo-acoustic tomography,
transient elastography, and magnetic resonance elastography. Stability
estimates are also proposed.Comment: 5 pages, announcement and extension of results obtained in
arXiv:1111.505
On sparsity averaging
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013)
introduced a novel regularization method for compressive imaging in the context
of compressed sensing with coherent redundant dictionaries. The approach relies
on the observation that natural images exhibit strong average sparsity over
multiple coherent frames. The associated reconstruction algorithm, based on an
analysis prior and a reweighted scheme, is dubbed Sparsity Averaging
Reweighted Analysis (SARA). We review these advances and extend associated
simulations establishing the superiority of SARA to regularization methods
based on sparsity in a single frame, for a generic spread spectrum acquisition
and for a Fourier acquisition of particular interest in radio astronomy.Comment: 4 pages, 3 figures, Proceedings of 10th International Conference on
Sampling Theory and Applications (SampTA), Code available at
https://github.com/basp-group/sopt, Full journal letter available at
http://arxiv.org/abs/arXiv:1208.233
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