The nonparametric estimation of convolutions is considered. Using the mean integrated squared error, we explore the performance of a plug-in estimator under mild assumptions on the model. We illustrate these general results via wavelet hard thresholding estimators for two different density estimation problems. In particular, we prove that they attain fast rates of convergence for a wide class of unknown functions. Simulation results illustrate the theory.