Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
复制标题
具有自调谐正则化参数的射电干涉多频图像重建
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
D. Mary
中科院分区：
文献类型：
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作者：
R. Ammanouil;A. Ferrari;Rémi Flamary;C. Ferrari;D. Mary
作为世界上最大的射电望远镜，平方公里阵列（SKA）将提供前所未有的详细无线电干涉数据。无线电干涉测量的图像重建算法面临的挑战是如何很好地扩展到以前从未见过的太字节图像大小。在这项工作中，我们研究了一个这样的三维图像重建算法称为MUFFIN（多频率图像重建无线电干涉测量）。特别是，我们专注于自动找到最佳正则化参数值的挑战性任务。在实践中，使用经典网格搜索找到正则化参数是计算密集型的，并且由于缺乏地面实况而非平凡的。我们采用了一种贪婪的策略，在每次迭代中，通过最小化预测的Stein无偏风险估计（PSURE）来找到最佳参数。所提出的自调整版本的MUFFIN涉及并行和计算效率高的步骤，以及大规模的数据扩展。最后，一个3D图像的数值结果来展示所提出的方法的性能。
As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground-truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large-scale data. Finally, numerical results on a 3D image are presented to showcase the performance of the proposed approach.
DOI：
10.1109/eusipco.2016.7760276
发表时间：
2016-08
期刊：
2016 24th European Signal Processing Conference (EUSIPCO)
影响因子：
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作者：
Abdullah Abdulaziz;A. Dabbech;Alexandru Onose;Y. Wiaux
通讯作者：
Abdullah Abdulaziz;A. Dabbech;Alexandru Onose;Y. Wiaux