Probabilistic image reconstruction for radio interferometers

  title={Probabilistic image reconstruction for radio interferometers},
  author={Paul Sutter and Benjamin D. Wandelt},
  journal={2014 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)},
  • P. Sutter, B. Wandelt
  • Published 5 September 2013
  • Computer Science
  • 2014 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)
We present a novel, general-purpose method for deconvolving and denoising images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model. The method automatically takes into account incomplete coverage of the uv-plane and mode coupling due to the beam. Our method uses Gibbs sampling to efficiently explore the full posterior distribution of the underlying signal image given the data. We use a set of widely diverse mock images with a realistic… 

Comparison of classical and Bayesian imaging in radio interferometry

This publication takes a VLA observation of Cygnus~A at four different frequencies and image it with single-scale CLEAN, multi-scaleCLEAN and resolve, and estimates a baseline-dependent correction function for the noise budget, the Bayesian equivalent of weighting schemes.

Hybrid Very Long Baseline Interferometry Imaging and Modeling with themis

Generating images from very long baseline interferometric observations poses a difficult, and generally not unique, inversion problem. This problem is simplified by the introduction of constraints,

Bayesian inference for radio observations

New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current

LOFAR sparse image reconstruction

This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKA.

Hermite-Gaussian functions for image synthesis

  • P. RománT. AsahiS. Casassus
  • Mathematics
    Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific
  • 2014
A non-local representation for the inverse problem is proposed based on Hermite-Gaussian (HG) functions, which are a complete set of eigenvectors for the Fourier operator, which is used for representing the problem.

Uncertainty quantification for radio interferometric imaging: II. MAP estimation

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.


Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or

Sparse representations and convex optimization as tools for LOFAR radio interferometric imaging

The theory behind interferometric imaging, sparse representations and convex optimization, and their application with numerical tests with SASIR, an implementation of the FISTA, a Forward-Backward splitting algorithm hosted in a LOFAR imager are exposed.

Simulation of the analysis of interferometric microwave background polarization data

Abstract We present results from an end-to-end simulation pipeline of interferometric observations of cosmic microwave background polarization. We use both maximum-likelihood and Gibbs sampling

High resolution VLBI polarization imaging of AGN with the maximum entropy method

Radio polarisation images of the jets of Active Galactic Nuclei (AGN) can provide a deep insight into the launching and collimation mechanisms of relativistic jets. However, even at VLBI scales,



Optimal Image Reconstruction in Radio Interferometry

We introduce a method for analyzing radio interferometry data that produces maps that are optimal in the Bayesian sense of maximum posterior probability density, given certain prior assumptions. It

Scale sensitive deconvolution of interferometric images - I. Adaptive Scale Pixel (Asp) decomposition

Deconvolution of the telescope Point Spread Function (PSF) is necessary for even moderate dynamic range imaging with interferometric telescopes. The process of deconvolution can be treated as a

Deconvolution of VLBI images based on compressive sensing

  • A. B. Suksmono
  • Physics
    2009 International Conference on Electrical Engineering and Informatics
  • 2009
It is shown that CS is well-suited with the VLBI imaging problem and demonstrated that the proposed method is capable to reconstruct a simulated image of radio galaxy from its incomplete visibility samples taken from elliptical trajectories in the uv-plane.

A multiple-beam CLEAN for imaging intra-day variable radio sources

The CLEAN algorithm, widely used in radio interferometry for the deconvolution of radio images, performs well only if the raw radio image (dirty image) is, to good approximation, a simple convolution

A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry

Aims. We describe MS-MFS, a multi-scale multi-frequency deconvolution algorithm for wide-band synthesis-imaging, and present imaging results that illustrate the capabilities of the alg orithm and the

PURIFY: a new approach to radio-interferometric imaging

The authors release a beta version of an SDMM-based imaging software written in C and dubbed PURIFY that handles various sparsity priors, including the recent average sparsity approach SARA, and evaluates the performance of different priors through simulations in the continuous visibility setting, confirming the superiority of SARA.

Multiscale CLEAN Deconvolution of Radio Synthesis Images

  • T. Cornwell
  • Computer Science
    IEEE Journal of Selected Topics in Signal Processing
  • 2008
This work describes and demonstrates a conceptually simple and algorithmically straightforward extension to CLEAN that models the sky brightness by the summation of components of emission having different size scales and works simultaneously on a range of specified scales.

Toward 5D image reconstruction for optical interferometry

This work uses the Monte Carlo Markov Chain software SQUEEZE to solve the image reconstruction problem on the surfaces of spotted stars, and the Compressed Sensing and Bayesian Evidence paradigms are employed to determine the best regularization for spotted stars.


We present a Bayesian angular power spectrum and signal map inference engine which can be adapted to interferometric observations of anisotropies in the cosmic microwave background (CMB), 21 cm

The application of compressive sampling to radio astronomy I: Deconvolution

A CS-based deconvolution method for extended sources that can reconstruct both point sources and extended sources and shows the best performance in deconvolving extended sources for both uniform and natural weighting of the sampled visibilities.