PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging
arXiv:1406.0359 · doi:10.1109/ICASSP.2014.6854636
Abstract
In recent works, compressed sensing (CS) and convex optimization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions, which leverage the versatility of convex optimization to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to significant acceleration of the reconstruction and high-dimensional data scalability. The new algorithmic structure promoted in a new software PURIFY (beta version) relies on the simultaneous-direction method of multipliers (SDMM). The performance of various sparsity priors is evaluated through simulations in the continuous visibility setting, confirming the superiority of our recent average sparsity approach SARA.
5 pages, 2 figures, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014, Related journal publication available at http://arxiv.org/abs/arXiv:1307.4370
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