Facet-Based Regularization for Scalable Radio-Interferometric Imaging

Current and future radio telescopes deal with large volumes of data and are expected to generate high resolution gigapixel-size images. The imaging problem in radio interferometry is highly ill-posed and the choice of prior model of the sky is of utmost importance to guarantee a reliable reconstruction. Traditionally, one or more...

Fast and accurate radio interferometric imaging using krylov subspaces

We propose a fast iterative method for image formation in Radio Astronomy (RA). We formulate the image formation problem as a maximum likelihood estimation problem to estimate the image pixel powers via array covariance measurements. We use an iterative solution method based on projections onto Krylov subspaces and exploit the sample...

PRIFIRA: General regularization using prior-conditioning for fast radio interferometric imaging

Image formation in radio astronomy is a large-scale inverse problem that is inherently illposed. We present a general algorithmic framework based on a Bayesian-inspired regularized maximum likelihood formulation of the radio astronomical imaging problem with a focus on diffuse emission recovery from limited noisy correlation data. The...

Radioastronomical image reconstruction with regularized least squares

Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization...