  Outdoor vision-based systems suffer from atmospheric turbulences, and rain is
one of the worst factors for vision degradation. Current rain removal methods
show limitations either for complex dynamic scenes, or under torrential rain
with opaque occlusions. We propose a novel derain framework which applies
superpixel (SP) segmentation to decompose the scene into depth consistent
units. Alignment of scene contents are done at the SP level, which proves to be
robust against rain occlusion interferences and fast camera motion. Two
alignment output tensors, i.e., optimal temporal match tensor and sorted
spatial-temporal match tensor, provide informative clues for the location of
rain streaks and the occluded background contents. Different classical and
novel methods such as Robust Principle Component Analysis and Convolutional
Neural Networks are applied and compared for their respective advantages in
efficiently exploiting the rich spatial-temporal features provided by the two
tensors. Extensive evaluations show that advantage of up to 5dB is achieved on
the scene restoration PSNR over state-of-the-art methods, and the advantage is
especially obvious with highly complex and dynamic scenes. Visual evaluations
show that the proposed framework is not only able to suppress heavy and opaque
occluding rain streaks, but also large semi-transparent regional fluctuations
and distortions.
