  Analysis $\ell_1$-recovery refers to a technique of recovering a signal that
is sparse in some transform domain from incomplete corrupted measurements. This
includes total variation minimization as an important special case when the
transform domain is generated by a difference operator. In the present paper we
provide a bound on the number of Gaussian measurements required for successful
recovery for total variation and for the case that the analysis operator is a
frame. The bounds are particularly suitable when the sparsity of the analysis
representation of the signal is not very small.
