An adaptive loop gain selection for CLEAN deconvolution algorithm

• Published in: Research in astronomy and astrophysics Vol. 19; no. 6; pp. 79 - 84
• Main Authors: Zhang, Li, Xu, Long, Zhang, Ming, Wu, Zhong-Zu
• Format: Journal Article
• Language: English
• Published: Beijing National Astronomical Observatories, CAS and IOP Publishing Ltd 01.06.2019; IOP Publishing
• Subjects: Algorithms; Data recovery; Deconvolution; Image reconstruction; Interferometry; methods: data analysis; Parameters; Robustness (mathematics); Steepest descent method; techniques: image processing; techniques: interferometric; Tuning
• ISSN: 1674-4527
• DOI: 10.1088/1674-4527/19/6/79

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 algorithmis 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.