  We present a method for improving segmentation tasks on images affected by
adherent rain drops and streaks. We introduce a novel stereo dataset recorded
using a system that allows one lens to be affected by real water droplets while
keeping the other lens clear. We train a denoising generator using this dataset
and show that it is effective at removing the effect of real water droplets, in
the context of image reconstruction and road marking segmentation. To further
test our de-noising approach, we describe a method of adding computer-generated
adherent water droplets and streaks to any images, and use this technique as a
proxy to demonstrate the effectiveness of our model in the context of general
semantic segmentation. We benchmark our results using the CamVid road marking
segmentation dataset, Cityscapes semantic segmentation datasets and our own
real-rain dataset, and show significant improvement on all tasks.
