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arXiv 1601.04026astro-ph.IM
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SKA时代大数据干涉成像的可扩展分裂算法

Scalable splitting algorithms for big-data interferometric imaging in the SKA era

Alexandru Onose, Rafael E. Carrillo, Audrey Repetti, Jason D. McEwen, Jean-Philippe Thiran, Jean-Christophe Pesquet, Yves Wiaux

摘要:

在下一代射电望远镜的背景下,如平方公里阵列,大规模数据集的有效处理是非常重要的。最近出现了压缩感知框架下的凸优化任务,并为越来越大的数据集提供了增强的图像重建质量和可扩展性。我们在这里主要集中在可扩展性,并提出了两个新的凸优化算法的结构,能够解决凸优化任务中出现的无线电干涉成像。它们依赖于邻近分裂和向前向后迭代,并且可以通过与CLEAN主-次循环类比来看到,在多个数据、先验和图像空间中并行运行复杂的CLEAN类迭代。这两种方法都支持任何凸正则化函数,特别是充分研究的l1先验在适当的域中促进图像稀疏性。它们针对大数据量身定制,采用并行和分布式计算来实现内存和计算需求方面的可扩展性。其中之一还利用了随机化,在每次迭代时对数据块进行随机化,提供了更大的灵活性。我们目前的仿真结果显示所提出的方法的可行性,以及他们的优势相比,国家的最先进的算法求解器。我们的Matlab代码可以在GitHub上在线获得。

英文摘要:

In the context of next generation radio telescopes, like the Square Kilometre Array, the efficient processing of large-scale datasets is extremely important. Convex optimisation tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We focus herein mainly on scalability and propose two new convex optimisation algorithmic structures able to solve the convex optimisation tasks arising in radio-interferometric imaging. They rely on proximal splitting and forward-backward iterations and can be seen, by analogy with the CLEAN major-minor cycle, as running sophisticated CLEAN-like iterations in parallel in multiple data, prior, and image spaces. Both methods support any convex regularisation function, in particular the well studied l1 priors promoting image sparsity in an adequate domain. Tailored for big-data, they employ parallel and distributed computations to achieve scalability, in terms of memory and computational requirements. One of them also exploits randomisation, over data blocks at each iteration, offering further flexibility. We present simulation results showing the feasibility of the proposed methods as well as their advantages compared to state-of-the-art algorithmic solvers. Our Matlab code is available online on GitHub.

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