The adaptive-loop-gain adaptive-scale CLEAN deconvolution of radio interferometric images
The adaptive-loop-gain adaptive-scale CLEAN deconvolution of radio interferometric images
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DOI：
10.1007/s10509-016-2746-8
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发表时间：
2016-04
影响因子：
1.9
通讯作者：
Luyuan Zhang;Mingwang Zhang;Xujia Liu
中科院分区：
文献类型：
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作者：
Luyuan Zhang;Mingwang Zhang;Xujia Liu
CLEAN算法是一类广泛用于消除望远镜点扩散函数（PSF）影响的反卷积求解器。环路增益是CLEAN算法中的一个重要参数。目前反褶积过程中的参数是固定的，这限制了CLEAN算法的性能。在本文中，我们提出了一种新的反卷积算法与自适应环路增益计划，这是被称为自适应环路增益自适应规模清洁（藻类清洁）算法。实验结果表明，新算法能给出更精确的模型，且收敛速度更快。
CLEAN algorithms are a class of deconvolution solvers which are widely used to remove the effect of the telescope Point Spread Function (PSF). Loop gain is one important parameter in CLEAN algorithms. Currently the parameter is fixed during deconvolution, which restricts the performance of CLEAN algorithms. In this paper, we propose a new deconvolution algorithm with an adaptive loop gain scheme, which is referred to as the adaptive-loop-gain adaptive-scale CLEAN (Algas-Clean) algorithm. The test results show that the new algorithm can give a more accurate model with faster convergence.