Deconvolution of VLBI Images Based on Compressive Sensing
  Direct inversion of incomplete visibility samples in VLBI (Very Large
Baseline Interferometry) radio telescopes produces images with convolutive
artifacts. Since proper analysis and interpretations of astronomical radio
sources require a non-distorted image, and because filling all of sampling
points in the uv-plane is an impossible task, image deconvolution has been one
of central issues in the VLBI imaging. Up to now, the most widely used
deconvolution algorithms are based on least-squares-optimization and maximum
entropy method. In this paper, we propose a new algorithm that is based on an
emerging paradigm called compressive sensing (CS). Under the sparsity
condition, CS capable to exactly reconstructs a signal or an image, using only
a few number of random samples. We show that CS is well-suited with the VLBI
imaging problem and demonstrate 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. The effectiveness
of the proposed method is also demonstrated with an actual VLBI measured data
of 3C459 asymmetric radio-galaxy observed by the VLA (Very Large Array).
