We propose a new technique to obtain super-resolution images with radio interferometry
using sparse modeling. In standard radio interferometry, sampling of (u, v) is quite often …

Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)
effectively separates the fault vibration signals of rolling bearings and improves the …

It is a classic problem to estimate continuous-time sparse signals, like point sources in a
direction-of-arrival problem, or pulses in a time-of-flight measurement. The earliest …

In a recent paper series, the authors have promoted convex optimization algorithms for radio-
interferometric imaging in the framework of compressed sensing, which leverages sparsity …

In the context of next-generation radio telescopes, like the Square Kilometre Array (SKA), the
efficient processing of large-scale data sets is extremely important. Convex optimization …

Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array
interferometer with multiple antennas distributed in Europe. It provides discrete sets of …

We propose a new imaging technique for interferometry using sparse modeling, utilizing two
regularization terms: the ℓ 1-norm and a new function named total squared variation (TSV) of …

An algorithmic framework to compute sparse or minimal-TV solutions of linear systems is
proposed. The framework includes both the Kaczmarz method and the linearized Bregman …

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 …

Abstract We leverage the Sparsity Averaging Re-weighted Analysis approach for
interferometric imaging, that is based on convex optimization, for the super-resolution of Cyg …