  The speckle phenomenon remains a major hurdle for the analysis of SAR images.
The development of speckle reduction methods closely follows methodological
progress in the field of image restoration. The advent of deep neural networks
has offered new ways to tackle this longstanding problem. Deep learning for
speckle reduction is a very active research topic and already shows restoration
performances that exceed that of the previous generations of methods based on
the concepts of patches, sparsity, wavelet transform or total variation
minimization. The objective of this paper is to give an overview of the most
recent works and point the main research directions and current challenges of
deep learning for SAR image restoration.
