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A Lq Proximal Gradient Algorithm for Radio-interferometric Imaging
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DOI：10.1088/1538-3873/adb334.png)
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Abstract
En 中文
The reconstruction from the measured visibilities to the signal in radio interferometry is an ill-posed inverse problem. The compressed sensing technology represented by the sparsity averaging reweighted analysis (SARA) has been successfully applied to radio-interferometric imaging. However, the traditional SARA algorithm solves the L-1 norm minimization problem instead of the L-0 norm one, which has a bias problem. In this paper, a L-q proximal gradient algorithm with 0 < q < 1 is proposed to ameliorate the bias problem and obtain an accurate solution in radio interferometry. The proposed method efficiently solves the L-q norm minimization problem by using the proximal gradient algorithm, and adopts restart and lazy-start strategies to reduce oscillations and accelerate the convergence rate. Numerical experiment results and quantitative analyses verify the effectiveness of the proposed method.
Keywords:
Radio interferometry
Radio telescopes
Interferometers
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