Deconvolution of VLBI Images Based on Compressive Sensing

TL;DR

This paper introduces a novel deconvolution algorithm for VLBI images based on compressive sensing, which effectively reconstructs images from incomplete data, outperforming traditional methods.

Contribution

The paper presents a new compressive sensing-based deconvolution algorithm tailored for VLBI imaging, improving image reconstruction from sparse visibility samples.

Findings

Successfully reconstructs simulated radio galaxy images from incomplete data.

Demonstrates effectiveness on real VLBI data of 3C459 radio galaxy.

Outperforms traditional deconvolution methods in accuracy.

Abstract

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).