University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Structured compressed sensing and recent theoretical advances on optimal sampling

Structured compressed sensing and recent theoretical advances on optimal sampling

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact info@newton.ac.uk.

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2018 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity