Multi-Scale CLEAN deconvolution of radio synthesis images

TL;DR

This paper introduces a multi-scale extension to the CLEAN deconvolution algorithm for radio synthesis imaging, improving the reconstruction of extended objects by modeling emission at multiple scales simultaneously.

Contribution

It presents a novel multi-scale CLEAN algorithm that operates on multiple scales simultaneously, enhancing image quality for extended radio sources.

Findings

Improved imaging of extended radio sources.

Effective application to real and simulated data.

Enhanced deconvolution performance over traditional CLEAN.

Abstract

Radio synthesis imaging is dependent upon deconvolution algorithms to counteract the sparse sampling of the Fourier plane. These deconvolution algorithms find an estimate of the true sky brightness from the necessarily incomplete sampled visibility data. The most widely used radio synthesis deconvolution method is the CLEAN algorithm of Hogbom. This algorithm works extremely well for collections of point sources and surprisingly well for extended objects. However, the performance for extended objects can be improved by adopting a multi-scale approach. We describe and demonstrate 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. While previous multiscale algorithms work sequentially on decreasing scale sizes, our algorithm works simultaneously on a range of specified scales. Applications to both real and simulated data sets are given.