  Reducing speckle and limiting the variations of the physical parameters in
Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the
potential of such data. Nowadays, deep learning approaches produce state of the
art results in single-image SAR restoration. Nevertheless, huge multi-temporal
stacks are now often available and could be efficiently exploited to further
improve image quality. This paper explores two fast strategies employing a
single-image despeckling algorithm, namely SAR2SAR, in a multi-temporal
framework. The first one is based on Quegan filter and replaces the local
reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress
speckle from a ratio image encoding the multi-temporal information under the
form of a "super-image", i.e. the temporal arithmetic mean of a time series.
Experimental results on Sentinel-1 GRD data show that these two multi-temporal
strategies provide improved filtering results while adding a limited
computational cost.
