  Deep learning approaches show unprecedented results for speckle reduction in
SAR amplitude images. The wide availability of multi-temporal stacks of SAR
images can improve even further the quality of denoising. In this paper, we
propose a flexible yet efficient way to integrate temporal information into a
deep neural network for speckle suppression. Archives provide access to long
time-series of SAR images, from which multi-temporal averages can be computed
with virtually no remaining speckle fluctuations. The proposed method combines
this multi-temporal average and the image at a given date in the form of a
ratio image and uses a state-of-the-art neural network to remove the speckle in
this ratio image. This simple strategy is shown to offer a noticeable
improvement compared to filtering the original image without knowledge of the
multi-temporal average.
