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Structured compressed sensing and recent theoretical advances on optimal sampling

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VMVW02 - Generative models, parameter learning and sparsity

Joint works with Jérémie Bigot and Pierre Weiss on the one hand, and Ben Adcock on the other hand. First, we will theoretically justify the applicability of compressed sensing (CS) in real-life applications. To do so, CS theorems compatible with physical acquisition constraints will be introduced. These results do not only encompass structure in the acquisition but also structured sparsity of the signal of interest. This theory considerably extends the standard framework of CS. Secondly, recent advances on optimal sampling in CS will be presented, in the sense that the sampling strategy minimizes the bound on the required number of measurements for CS recovery.

This talk is part of the Isaac Newton Institute Seminar Series series.

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