215 research outputs found
Localisation of directional scale-discretised wavelets on the sphere
Scale-discretised wavelets yield a directional wavelet framework on the
sphere where a signal can be probed not only in scale and position but also in
orientation. Furthermore, a signal can be synthesised from its wavelet
coefficients exactly, in theory and practice (to machine precision).
Scale-discretised wavelets are closely related to spherical needlets (both were
developed independently at about the same time) but relax the axisymmetric
property of needlets so that directional signal content can be probed. Needlets
have been shown to satisfy important quasi-exponential localisation and
asymptotic uncorrelation properties. We show that these properties also hold
for directional scale-discretised wavelets on the sphere and derive similar
localisation and uncorrelation bounds in both the scalar and spin settings.
Scale-discretised wavelets can thus be considered as directional needlets.Comment: 28 pages, 8 figures, minor changes to match version accepted for
publication by ACH
Uncertainty quantification for radio interferometric imaging: II. MAP estimation
Uncertainty quantification is a critical missing component in radio
interferometric imaging that will only become increasingly important as the
big-data era of radio interferometry emerges. Statistical sampling approaches
to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling,
can in principle recover the full posterior distribution of the image, from
which uncertainties can then be quantified. However, for massive data sizes,
like those anticipated from the Square Kilometre Array (SKA), it will be
difficult if not impossible to apply any MCMC technique due to its inherent
computational cost. We formulate Bayesian inference problems with
sparsity-promoting priors (motivated by compressive sensing), for which we
recover maximum a posteriori (MAP) point estimators of radio interferometric
images by convex optimisation. Exploiting recent developments in the theory of
probability concentration, we quantify uncertainties by post-processing the
recovered MAP estimate. Three strategies to quantify uncertainties are
developed: (i) highest posterior density credible regions; (ii) local credible
intervals (cf. error bars) for individual pixels and superpixels; and (iii)
hypothesis testing of image structure. These forms of uncertainty
quantification provide rich information for analysing radio interferometric
observations in a statistically robust manner. Our MAP-based methods are
approximately times faster computationally than state-of-the-art MCMC
methods and, in addition, support highly distributed and parallelised
algorithmic structures. For the first time, our MAP-based techniques provide a
means of quantifying uncertainties for radio interferometric imaging for
realistic data volumes and practical use, and scale to the emerging big-data
era of radio astronomy.Comment: 13 pages, 10 figures, see companion article in this arXiv listin
Slepian Spatial-Spectral Concentration on the Ball
We formulate and solve the Slepian spatial-spectral concentration problem on
the three-dimensional ball. Both the standard Fourier-Bessel and also the
Fourier-Laguerre spectral domains are considered since the latter exhibits a
number of practical advantages (spectral decoupling and exact computation). The
Slepian spatial and spectral concentration problems are formulated as
eigenvalue problems, the eigenfunctions of which form an orthogonal family of
concentrated functions. Equivalence between the spatial and spectral problems
is shown. The spherical Shannon number on the ball is derived, which acts as
the analog of the space-bandwidth product in the Euclidean setting, giving an
estimate of the number of concentrated eigenfunctions and thus the dimension of
the space of functions that can be concentrated in both the spatial and
spectral domains simultaneously. Various symmetries of the spatial region are
considered that reduce considerably the computational burden of recovering
eigenfunctions, either by decoupling the problem into smaller subproblems or by
affording analytic calculations. The family of concentrated eigenfunctions
forms a Slepian basis that can be used be represent concentrated signals
efficiently. We illustrate our results with numerical examples and show that
the Slepian basis indeeds permits a sparse representation of concentrated
signals.Comment: 33 pages, 10 figure
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
PURIFY: a new approach to radio-interferometric imaging
In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a minimization problem for image reconstruction. This approach was shown, in theory and through simulations in a simple discrete visibility setting, to have the potential to outperform significantly CLEAN and its evolutions. In this work, we leverage the versatility of convex optimization in solving minimization problems to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to significant acceleration of the reconstruction and high-dimensional data scalability. The new algorithmic structure promoted relies on the simultaneous-direction method of multipliers (SDMM), and contrasts with the current major-minor cycle structure of CLEAN and its evolutions, which in particular cannot handle the state-of-the-art minimization problems under consideration where neither the regularization term nor the data term are differentiable functions. We release a beta version of an SDMM-based imaging software written in C and dubbed PURIFY (http://basp-group.github.io/purify/) that handles various sparsity priors, including our recent average sparsity approach SARA. We evaluate the performance of different priors through simulations in the continuous visibility setting, confirming the superiority of SARA
PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging
In recent works, compressed sensing (CS) and convex opti- mization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions [1, 2, 3], which leverage the versatility of convex optimization to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to significant acceleration of the reconstruction and high-dimensional data scalability. The new algorithmic structure promoted in a new software PURIFY (beta version) relies on the simultaneous-direction method of multipliers (SDMM). The performance of various sparsity priors is evaluated through simulations in the continuous visibility setting, confirming the superiority of our recent average sparsity approach SARA
A fast and exact -stacking and -projection hybrid algorithm for wide-field interferometric imaging
The standard wide-field imaging technique, the -projection, allows
correction for wide-fields of view for non-coplanar radio interferometric
arrays. However, calculating exact corrections for each measurement has not
been possible due to the amount of computation required at high resolution and
with the large number of visibilities from current interferometers. The
required accuracy and computational cost of these corrections is one of the
largest unsolved challenges facing next generation radio interferometers such
as the Square Kilometre Array. We show that the same calculation can be
performed with a radially symmetric -projection kernel, where we use one
dimensional adaptive quadrature to calculate the resulting Hankel transform,
decreasing the computation required for kernel generation by several orders of
magnitude, whilst preserving the accuracy. We confirm that the radial
-projection kernel is accurate to approximately 1% by imaging the
zero-spacing with an added -term. We demonstrate the potential of our
radially symmetric -projection kernel via sparse image reconstruction, using
the software package PURIFY. We develop a distributed -stacking and
-projection hybrid algorithm. We apply this algorithm to individually
correct for non-coplanar effects in 17.5 million visibilities over a by
degree field of view MWA observation for image reconstruction. Such a
level of accuracy and scalability is not possible with standard -projection
kernel generation methods. This demonstrates that we can scale to a large
number of measurements with large image sizes whilst still maintaining both
speed and accuracy.Comment: 9 Figures, 19 Pages. Accepted to Ap
- …