  In SAR domain many application like classification, detection and
segmentation are impaired by speckle. Hence, despeckling of SAR images is the
key for scene understanding. Usually despeckling filters face the trade-off of
speckle suppression and information preservation. In the last years deep
learning solutions for speckle reduction have been proposed. One the biggest
issue for these methods is how to train a network given the lack of a
reference. In this work we proposed a convolutional neural network based
solution trained on simulated data. We propose the use of a cost function
taking into account both spatial and statistical properties. The aim is two
fold: overcome the trade-off between speckle suppression and details
suppression; find a suitable cost function for despeckling in unsupervised
learning. The algorithm is validated on both real and simulated data, showing
interesting performances.
