14 research outputs found
Signal Reconstruction from Modulo Observations
We consider the problem of reconstructing a signal from under-determined
modulo observations (or measurements). This observation model is inspired by a
(relatively) less well-known imaging mechanism called modulo imaging, which can
be used to extend the dynamic range of imaging systems; variations of this
model have also been studied under the category of phase unwrapping. Signal
reconstruction in the under-determined regime with modulo observations is a
challenging ill-posed problem, and existing reconstruction methods cannot be
used directly. In this paper, we propose a novel approach to solving the
inverse problem limited to two modulo periods, inspired by recent advances in
algorithms for phase retrieval under sparsity constraints. We show that given a
sufficient number of measurements, our algorithm perfectly recovers the
underlying signal and provides improved performance over other existing
algorithms. We also provide experiments validating our approach on both
synthetic and real data to depict its superior performance
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
The classical problem of phase retrieval arises in various signal acquisition
systems. Due to the ill-posed nature of the problem, the solution requires
assumptions on the structure of the signal. In the last several years, sparsity
and support-based priors have been leveraged successfully to solve this
problem. In this work, we propose replacing the sparsity/support priors with
generative priors and propose two algorithms to solve the phase retrieval
problem. Our proposed algorithms combine the ideas from AltMin approach for
non-convex sparse phase retrieval and projected gradient descent approach for
solving linear inverse problems using generative priors. We empirically show
that the performance of our method with projected gradient descent is superior
to the existing approach for solving phase retrieval under generative priors.
We support our method with an analysis of sample complexity with Gaussian
measurements.Comment: Published in ICASSP 201
Phase Retrieval by Alternating Minimization with Random Initialization
We consider a phase retrieval problem, where the goal is to reconstruct a
-dimensional complex vector from its phaseless scalar products with
sensing vectors, independently sampled from complex normal distributions. We
show that, with a random initialization, the classical algorithm of alternating
minimization succeeds with high probability as when
for some . This is a step toward
proving the conjecture in \cite{Waldspurger2016}, which conjectures that the
algorithm succeeds when . The analysis depends on an approach that
enables the decoupling of the dependency between the algorithmic iterates and
the sensing vectors
Compressive Phase Retrieval via Reweighted Amplitude Flow
The problem of reconstructing a sparse signal vector from magnitude-only
measurements (a.k.a., compressive phase retrieval), emerges naturally in
diverse applications, but it is NP-hard in general. Building on recent advances
in nonconvex optimization, this paper puts forth a new algorithm that is termed
compressive reweighted amplitude flow and abbreviated as CRAF, for compressive
phase retrieval. Specifically, CRAF operates in two stages. The first stage
seeks a sparse initial guess via a new spectral procedure. In the second stage,
CRAF implements a few hard thresholding based iterations using reweighted
gradients. When there are sufficient measurements, CRAF provably recovers the
underlying signal vector exactly with high probability under suitable
conditions. Moreover, its sample complexity coincides with that of the
state-of-the-art procedures. Finally, substantial simulated tests showcase
remarkable performance of the new spectral initialization, as well as improved
exact recovery relative to competing alternatives
Accelerated Wirtinger Flow: A fast algorithm for ptychography
This paper presents a new algorithm, Accelerated Wirtinger Flow (AWF), for
ptychographic image reconstruction from phaseless diffraction pattern
measurements. AWF is based on combining Nesterov's acceleration approach with
Wirtinger gradient descent. Theoretical results enable prespecification of all
AWF algorithm parameters, with no need for computationally-expensive line
searches and no need for manual parameter tuning. AWF is evaluated in the
context of simulated X-ray ptychography, where we demonstrate fast convergence
and low per-iteration computational complexity. We also show examples where AWF
reaches higher image quality with less computation than classical algorithms.
AWF is also shown to have robustness to noise and probe misalignment
Learning Illumination Patterns for Coded Diffraction Phase Retrieval
Signal recovery from nonlinear measurements involves solving an iterative
optimization problem. In this paper, we present a framework to optimize the
sensing parameters to improve the quality of the signal recovered by the given
iterative method. In particular, we learn illumination patterns to recover
signals from coded diffraction patterns using a fixed-cost alternating
minimization-based phase retrieval method. Coded diffraction phase retrieval is
a physically realistic system in which the signal is first modulated by a
sequence of codes before the sensor records its Fourier amplitude. We represent
the phase retrieval method as an unrolled network with a fixed number of layers
and minimize the recovery error by optimizing over the measurement parameters.
Since the number of iterations/layers are fixed, the recovery incurs a fixed
cost. We present extensive simulation results on a variety of datasets under
different conditions and a comparison with existing methods. Our results
demonstrate that the proposed method provides near-perfect reconstruction using
patterns learned with a small number of training images. Our proposed method
provides significant improvements over existing methods both in terms of
accuracy and speed
Phase retrieval of complex-valued objects via a randomized Kaczmarz method
This paper investigates the convergence of the randomized Kaczmarz algorithm
for the problem of phase retrieval of complex-valued objects. While this
algorithm has been studied for the real-valued case}, its generalization to the
complex-valued case is nontrivial and has been left as a conjecture. This paper
establishes the connection between the convergence of the algorithm and the
convexity of an objective function. Based on the connection, it demonstrates
that when the sensing vectors are sampled uniformly from a unit sphere and the
number of sensing vectors satisfies as , then this algorithm with a good initialization achieves
linear convergence to the solution with high probability
Solving Almost all Systems of Random Quadratic Equations
This paper deals with finding an -dimensional solution to a system of
quadratic equations of the form for , which is also known as phase retrieval and is NP-hard in general. We put
forth a novel procedure for minimizing the amplitude-based least-squares
empirical loss, that starts with a weighted maximal correlation initialization
obtainable with a few power or Lanczos iterations, followed by successive
refinements based upon a sequence of iteratively reweighted (generalized)
gradient iterations. The two (both the initialization and gradient flow) stages
distinguish themselves from prior contributions by the inclusion of a fresh
(re)weighting regularization technique. The overall algorithm is conceptually
simple, numerically scalable, and easy-to-implement. For certain random
measurement models, the novel procedure is shown capable of finding the true
solution in time proportional to reading the data . This holds with high probability and without extra assumption on the
signal to be recovered, provided that the number of equations is some
constant times the number of unknowns in the signal vector, namely,
. Empirically, the upshots of this contribution are: i) (almost)
perfect signal recovery in the high-dimensional (say e.g., ) regime
given only an information-theoretic limit number of noiseless equations,
namely, in the real-valued Gaussian case; and, ii) (nearly) optimal
statistical accuracy in the presence of additive noise of bounded support.
Finally, substantial numerical tests using both synthetic data and real images
corroborate markedly improved signal recovery performance and computational
efficiency of our novel procedure relative to state-of-the-art approaches.Comment: 27 pages, 8 figure
Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements
We consider the problem of recovering a signal , from magnitude-only measurements for . Also called
the phase retrieval, this is a fundamental challenge in bio-,astronomical
imaging and speech processing. The problem above is ill-posed; additional
assumptions on the signal and/or the measurements are necessary. In this paper
we first study the case where the signal is -sparse. We
develop a novel algorithm that we call Compressive Phase Retrieval with
Alternating Minimization, or CoPRAM. Our algorithm is simple; it combines the
classical alternating minimization approach for phase retrieval with the CoSaMP
algorithm for sparse recovery. Despite its simplicity, we prove that CoPRAM
achieves a sample complexity of with Gaussian measurements
, matching the best known existing results; moreover, it
demonstrates linear convergence in theory and practice. Additionally, it
requires no extra tuning parameters other than signal sparsity and is
robust to noise. When the sorted coefficients of the sparse signal exhibit a
power law decay, we show that CoPRAM achieves a sample complexity of , which is close to the information-theoretic limit. We also consider the
case where the signal arises from structured sparsity models. We
specifically examine the case of block-sparse signals with uniform block size
of and block sparsity . For this problem, we design a recovery
algorithm Block CoPRAM that further reduces the sample complexity to . For sufficiently large block lengths of , this bound equates
to . To our knowledge, this constitutes the first end-to-end
algorithm for phase retrieval where the Gaussian sample complexity has a
sub-quadratic dependence on the signal sparsity level
Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow
This paper presents a new algorithm, termed \emph{truncated amplitude flow}
(TAF), to recover an unknown vector from a system of quadratic
equations of the form , where
's are given random measurement vectors. This problem is known to be
\emph{NP-hard} in general. We prove that as soon as the number of equations is
on the order of the number of unknowns, TAF recovers the solution exactly (up
to a global unimodular constant) with high probability and complexity growing
linearly with both the number of unknowns and the number of equations. Our TAF
approach adopts the \emph{amplitude-based} empirical loss function, and
proceeds in two stages. In the first stage, we introduce an
\emph{orthogonality-promoting} initialization that can be obtained with a few
power iterations. Stage two refines the initial estimate by successive updates
of scalable \emph{truncated generalized gradient iterations}, which are able to
handle the rather challenging nonconvex and nonsmooth amplitude-based objective
function. In particular, when vectors and 's are
real-valued, our gradient truncation rule provably eliminates erroneously
estimated signs with high probability to markedly improve upon its untruncated
version. Numerical tests using synthetic data and real images demonstrate that
our initialization returns more accurate and robust estimates relative to
spectral initializations. Furthermore, even under the same initialization, the
proposed amplitude-based refinement outperforms existing Wirtinger flow
variants, corroborating the superior performance of TAF over state-of-the-art
algorithms.Comment: 37 Pages, 16 figure