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

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