Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

In recent works, sparse models and convex optimization techniques have been applied to radio-interferometric (RI) imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. In this talk, I will review our latest contributions in RI imaging, which leverage the versatility of convex optimization to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to high-dimensional data scalability. Firstly, I will review our recently proposed average sparsity approach, SARA, which relies on the observation that natural images exhibit strong average sparsity over multiple coherent bases. Secondly, I will discuss efficient implementations of SARA, and sparse regularization problems in general, for large-scale imaging problems in a new toolbox dubbed PURIFY.

Presented at:
International Biomedical and Astronomical Signal Processing (BASP) Frontiers workshop, Villars-sur-Ollon, Switzerland, January, 2015

 Record created 2015-02-04, last modified 2018-01-28

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