133 research outputs found
Coherent Diffractive Imaging Using Randomly Coded Masks
Coherent diffractive imaging (CDI) provides new opportunities for high
resolution X-ray imaging with simultaneous amplitude and phase contrast.
Extensions to CDI broaden the scope of the technique for use in a wide variety
of experimental geometries and physical systems. Here, we experimentally
demonstrate a new extension to CDI that encodes additional information through
the use of a series of randomly coded masks. The information gained from the
few additional diffraction measurements removes the need for typical
object-domain constraints; the algorithm uses prior information about the masks
instead. The experiment is performed using a laser diode at 532.2 nm, enabling
rapid prototyping for future X-ray synchrotron and even free electron laser
experiments. Diffraction patterns are collected with up to 15 different masks
placed between a CCD detector and a single sample. Phase retrieval is performed
using a convex relaxation routine known as "PhaseCut" followed by a variation
on Fienup's input-output algorithm. The reconstruction quality is judged via
calculation of phase retrieval transfer functions as well as by an object-space
comparison between reconstructions and a lens-based image of the sample. The
results of this analysis indicate that with enough masks (in this case 3 or 4)
the diffraction phases converge reliably, implying stability and uniqueness of
the retrieved solution
PhasePack: A Phase Retrieval Library
Phase retrieval deals with the estimation of complex-valued signals solely
from the magnitudes of linear measurements. While there has been a recent
explosion in the development of phase retrieval algorithms, the lack of a
common interface has made it difficult to compare new methods against the
state-of-the-art. The purpose of PhasePack is to create a common software
interface for a wide range of phase retrieval algorithms and to provide a
common testbed using both synthetic data and empirical imaging datasets.
PhasePack is able to benchmark a large number of recent phase retrieval methods
against one another to generate comparisons using a range of different
performance metrics. The software package handles single method testing as well
as multiple method comparisons.
The algorithm implementations in PhasePack differ slightly from their
original descriptions in the literature in order to achieve faster speed and
improved robustness. In particular, PhasePack uses adaptive stepsizes,
line-search methods, and fast eigensolvers to speed up and automate
convergence
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
Multiple Illumination Phaseless Super-Resolution (MIPS) with Applications To Phaseless DOA Estimation and Diffraction Imaging
Phaseless super-resolution is the problem of recovering an unknown signal
from measurements of the magnitudes of the low frequency Fourier transform of
the signal. This problem arises in applications where measuring the phase, and
making high-frequency measurements, are either too costly or altogether
infeasible. The problem is especially challenging because it combines the
difficult problems of phase retrieval and classical super-resolutionComment: To appear in ICASSP 201
Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient
Fourier ptychographic microscopy (FPM) is a novel computational coherent
imaging technique for high space-bandwidth product imaging. Mathematically,
Fourier ptychographic (FP) reconstruction can be implemented as a phase
retrieval optimization process, in which we only obtain low resolution
intensity images corresponding to the sub-bands of the sample's high resolution
(HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real
setups, the measurements always suffer from various degenerations such as
Gaussian noise, Poisson noise, speckle noise and pupil location error, which
would largely degrade the reconstruction. To efficiently address these
degenerations, we propose a novel FP reconstruction method under a gradient
descent optimization framework in this paper. The technique utilizes Poisson
maximum likelihood for better signal modeling, and truncated Wirtinger gradient
for error removal. Results on both simulated data and real data captured using
our laser FPM setup show that the proposed method outperforms other
state-of-the-art algorithms. Also, we have released our source code for
non-commercial use
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