  I discuss approaches to optimally remove noise from images. A generalization
of Wiener filtering to Non-Gaussian distributions and wavelets is described, as
well as an approach to measure the errors in the reconstructed images. We argue
that the wavelet basis is highly advantageous over either Fourier or real space
analysis if the data is intermittent in nature, i.e. if the filling factor of
objects is small.
