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anadaptiveloopgainselectionforcleandeconvolutionalgorithm
Zhang Li1; Xu Long2; Zhang Ming3; Wu Zhongzu1
2019
Source Publicationresearchinastronomyandastrophysics
ISSN1674-4527
Volume19Issue:6
AbstractRadio 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 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.
Language英语
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/37857
Collection中国科学院国家天文台
Affiliation1.贵州大学
2.中国科学院国家天文台
3.中国科学院新疆天文台
Recommended Citation
GB/T 7714
Zhang Li,Xu Long,Zhang Ming,et al. anadaptiveloopgainselectionforcleandeconvolutionalgorithm[J]. researchinastronomyandastrophysics,2019,19(6).
APA Zhang Li,Xu Long,Zhang Ming,&Wu Zhongzu.(2019).anadaptiveloopgainselectionforcleandeconvolutionalgorithm.researchinastronomyandastrophysics,19(6).
MLA Zhang Li,et al."anadaptiveloopgainselectionforcleandeconvolutionalgorithm".researchinastronomyandastrophysics 19.6(2019).
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