Journal Article

Parameterized CLEAN Deconvolution in Radio Synthesis Imaging

Zhang, L., Zhang, M., Xu, L.

2020

Overview

This paper reviews parameterized CLEAN deconvolution, which is widely used in radio synthesis imaging to remove the effects of sidelobes from the point-spread function caused by incomplete sampling by the radio telescope array. At the same time, different forms of parameterization and components are provided, as well as methods for approximating the true sky brightness. In recent years, a large number of variants of the CLEAN algorithm have been proposed  to deliver faster and better reconstruction of extended emission. The diversity of algorithms  has stemmed from the need to deal with different situations  as well as  optimizing the previous algorithms. In this paper, these CLEAN deconvolution algorithms are classified as scale-free, multi-scale and adaptive-scale deconvolution algorithms  based on  their different  sky-parameterization methods. In general,  scale-free algorithms are more efficient when dealing with compact sources, while  multi-scale and adaptive-scale algorithms are more efficient when handing extended sources. We will cover the details of these algorithms, such as how they handle the background, their parameterization and the differences between  them.  In particular, we discuss the latest algorithm,  which has been able to efficiently handle both compact and extended sources simultaneously  via the deep integration of scale-free and adaptive-scale algorithms. We also mentioned recent developments in other important deconvolution methods and compared them with CLEAN deconvolution.

• Publisher: IOP Publishing Limited
• Subject: Topical Review