  SAR images are affected by multiplicative noise that impairs their
interpretations. In the last decades several methods for SAR denoising have
been proposed and in the last years great attention has moved towards deep
learning based solutions. Based on our last proposed convolutional neural
network for SAR despeckling, here we exploit the effect of the complexity of
the network. More precisely, once a dataset has been fixed, we carry out an
analysis of the network performance with respect to the number of layers and
numbers of features the network is composed of. Evaluation on simulated and
real data are carried out. The results show that deeper networks better
generalize on both simulated and real images.
