Long measurement times due to low sensitivity are a prime concern in solid-state NMR and limit the application of multidimensional experiments severely. One possibility to address this problem could be post-experimental suppression of noise and a reduction of the number of increments needed for higher dimensional data sets. This can be achieved by a hybrid approach based on the combination of separately Fourier transformed and covariance processed datasets. The method is applied to synthetic sets as well as to experimental two-dimensional homonuclear solid-state NMR spectra of peptide samples. It is demonstrated that a reduction in experiment time by a factor of 4 can be achieved for the case of a 13C-13C correlation spectrum on the nonapeptide bradykinin.