An adaptive loop gain selection for CLEAN deconvolution algorithm

• 来源期刊: 天文和天体物理学研究
• 年，卷（期）: 2019,（6）
• 页码范围: 37-42
• 页数: 6页
• 语种: 英文
• DOI: 10.1088/1674-4527/19/6/79

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 (PSNR-based) 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.