n 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.
|Number of pages||1|
|Publication status||Published - 2015|
|Event||BASP Frontiers 2015 - Villars-sur-Ollon, Lausanne, Switzerland|
Duration: 25 Jan 2015 → 30 Jan 2015
|Workshop||BASP Frontiers 2015|
|Period||25/01/15 → 30/01/15|