Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging
Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging
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DOI：
10.1111/j.1365-2966.2012.21605.x
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发表时间：
2012-10-01
影响因子：
4.8
通讯作者：
Wiaux, Y.
中科院分区：
文献类型：
--
作者：
Carrillo, R. E.;McEwen, J. D.;Wiaux, Y.
提出了一种新的射电干涉图像重建算法。由能见度测量识别的不完全傅立叶采样引起的不适定反问题通过多个小波基表示的平均信号稀疏性的假设被正则化。该算法是在凸优化的通用框架中定义的，被称为稀疏性平均重加权分析。仿真结果表明，该方法优于目前最先进的基于信号稀疏性假设的成像方法。
We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by the visibility measurements is regularized by the assumption of average signal sparsity over representations in multiple wavelet bases. The algorithm, defined in the versatile framework of convex optimization, is dubbed Sparsity Averaging Reweighted Analysis. We show through simulations that the proposed approach outperforms state-of-the-art imaging methods in the field, which are based on the assumption of signal sparsity in a single basis only.