  This paper provides a new tractable lower bound for the sparse recovery
threshold of sensing matrices. This lower bound is used as a proxy to quantify
the quality of sensing matrices in two different applications. First, it serves
as regularization for the classical dictionary learning problem in order to
learn dictionaries with better generalisation properties on unseen data. Then,
the proxy is used to design sampling schemes for MRI acquisition that exhibit
high reconstruction performances.
