An adaptive loop gain selection for CLEAN deconvolution algorithm

论文摘要

Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN（CS-Clean） deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based（PSNRbased） method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.

论文目录

• 1 INTRODUCTION
• 2 CLEAN ALGORITHM
• 3 THE NEW ALGORITHM
• 4 NUMERICAL EXPERIMENT
• 5 SUMMARY

文章来源

类型: 期刊论文

作者: Li Zhang,Long Xu,Ming Zhang,Zhong-Zu Wu

来源: Research in Astronomy and Astrophysics 2019年06期

年度: 2019

分类: 基础科学

专业: 天文学

单位: College of Big Data and Information Engineering, Guizhou University,Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences,Xinjiang Astronomical Observatory, Chinese Academy of Sciences,Key Laboratory of Radio Astronomy, Chinese Academy of Sciences,College of Physics, Guizhou University

基金: partially supported by the Open Research Program of the CAS Key Laboratory of Solar Activity (KLSA201805),the Guizhou Science & Technology Plan Project (Platform Talent No.[2017]5788),the Youth Science & Technology Talents Development Project of Guizhou Education Department (No. KY[2018]119),the National Science Foundation of China (Grant Nos. 11103055,11773062 and 61605153),“Light of West China” Programme (Grant Nos. RCPY201105 and 2017-XBQNXZ-A-008),the National Basic Research Program of China (973 program: 2012CB821804 and 2015CB857100)

分类号: P161

页码: 39-44

总页数: 6

文件大小: 1367K

下载量: 10