  Speckle fluctuations seriously limit the interpretability of synthetic
aperture radar (SAR) images. Speckle reduction has thus been the subject of
numerous works spanning at least four decades. Techniques based on deep neural
networks have recently achieved a new level of performance in terms of SAR
image restoration quality. Beyond the design of suitable network architectures
or the selection of adequate loss functions, the construction of training sets
is of uttermost importance. So far, most approaches have considered a
supervised training strategy: the networks are trained to produce outputs as
close as possible to speckle-free reference images. Speckle-free images are
generally not available, which requires resorting to natural or optical images
or the selection of stable areas in long time series to circumvent the lack of
ground truth. Self-supervision, on the other hand, avoids the use of
speckle-free images. We introduce a self-supervised strategy based on the
separation of the real and imaginary parts of single-look complex SAR images,
called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a
straightforward way to train all kinds of deep despeckling networks. Networks
trained with MERLIN take into account the spatial correlations due to the SAR
transfer function specific to a given sensor and imaging mode. By requiring
only a single image, and possibly exploiting large archives, MERLIN opens the
door to hassle-free as well as large-scale training of despeckling networks.
The code of the trained models is made freely available at
https://gitlab.telecom-paris.fr/RING/MERLIN.
