66 research outputs found
Phase retrieval with polarization
In many areas of imaging science, it is difficult to measure the phase of
linear measurements. As such, one often wishes to reconstruct a signal from
intensity measurements, that is, perform phase retrieval. In this paper, we
provide a novel measurement design which is inspired by interferometry and
exploits certain properties of expander graphs. We also give an efficient phase
retrieval procedure, and use recent results in spectral graph theory to produce
a stable performance guarantee which rivals the guarantee for PhaseLift in
[Candes et al. 2011]. We use numerical simulations to illustrate the
performance of our phase retrieval procedure, and we compare reconstruction
error and runtime with a common alternating-projections-type procedure
Phase retrieval from power spectra of masked signals
In diffraction imaging, one is tasked with reconstructing a signal from its
power spectrum. To resolve the ambiguity in this inverse problem, one might
invoke prior knowledge about the signal, but phase retrieval algorithms in this
vein have found limited success. One alternative is to create redundancy in the
measurement process by illuminating the signal multiple times, distorting the
signal each time with a different mask. Despite several recent advances in
phase retrieval, the community has yet to construct an ensemble of masks which
uniquely determines all signals and admits an efficient reconstruction
algorithm. In this paper, we leverage the recently proposed polarization method
to construct such an ensemble. We also present numerical simulations to
illustrate the stability of the polarization method in this setting. In
comparison to a state-of-the-art phase retrieval algorithm known as PhaseLift,
we find that polarization is much faster with comparable stability.Comment: 18 pages, 3 figure
Robust phase retrieval with the swept approximate message passing (prSAMP) algorithm
In phase retrieval, the goal is to recover a complex signal from the
magnitude of its linear measurements. While many well-known algorithms
guarantee deterministic recovery of the unknown signal using i.i.d. random
measurement matrices, they suffer serious convergence issues some
ill-conditioned matrices. As an example, this happens in optical imagers using
binary intensity-only spatial light modulators to shape the input wavefront.
The problem of ill-conditioned measurement matrices has also been a topic of
interest for compressed sensing researchers during the past decade. In this
paper, using recent advances in generic compressed sensing, we propose a new
phase retrieval algorithm that well-adopts for both Gaussian i.i.d. and binary
matrices using both sparse and dense input signals. This algorithm is also
robust to the strong noise levels found in some imaging applications
Stable phase retrieval with low-redundancy frames
We investigate the recovery of vectors from magnitudes of frame coefficients
when the frames have a low redundancy, meaning a small number of frame vectors
compared to the dimension of the Hilbert space. We first show that for vectors
in d dimensions, 4d-4 suitably chosen frame vectors are sufficient to uniquely
determine each signal, up to an overall unimodular constant, from the
magnitudes of its frame coefficients. Then we discuss the effect of noise and
show that 8d-4 frame vectors provide a stable recovery if part of the frame
coefficients is bounded away from zero. In this regime, perturbing the
magnitudes of the frame coefficients by noise that is sufficiently small
results in a recovery error that is at most proportional to the noise level.Comment: 12 pages AMSLaTeX, 1 figur
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