Professor Massimo Fornasier: An introduction to compressed sensing and recent advances

Compressive sensing is a new type of sampling theory, which predicts that
sparse vectors can be reconstructed from what was previously believed to be
incomplete information. As a main feature, efficient algorithms such as
ℓ1-minimization can be used for recovery. The theory has many potential applications in
signal processing and imaging. This talk gives an introduction and overview on both
theoretical and numerical aspects of compressive sensing.

Suggested literature:
http://www.ricam.oeaw.ac.at/people/page/fornasier/CSFornasierRauhut.pdf