Scientific Computation and Applied & Industrial Mathematics: Claire Boyer

The talk will be divided into 2 parts. First, we will theoretically justify the applicability of compressed sensing (CS) in real-life applications. To do so, I will introduce CS theorems compatible with physical acquisition constraints. These new results do not only encompass structure in the acquisition but also structured sparsity of the signal of interest. Then, we will present a new way to generate subsampling schemes that can be implemented on real sensors and that give good reconstruction results. This work relies on measure projection and will be illustrated in the case of MRI.