  Single image rain removal is a typical inverse problem in computer vision.
The deep learning technique has been verified to be effective for this task and
achieved state-of-the-art performance. However, previous deep learning methods
need to pre-collect a large set of image pairs with/without synthesized rain
for training, which tends to make the neural network be biased toward learning
the specific patterns of the synthesized rain, while be less able to generalize
to real test samples whose rain types differ from those in the training data.
To this issue, this paper firstly proposes a semi-supervised learning paradigm
toward this task. Different from traditional deep learning methods which only
use supervised image pairs with/without synthesized rain, we further put real
rainy images, without need of their clean ones, into the network training
process. This is realized by elaborately formulating the residual between an
input rainy image and its expected network output (clear image without rain) as
a specific parametrized rain streaks distribution. The network is therefore
trained to adapt real unsupervised diverse rain types through transferring from
the supervised synthesized rain, and thus both the short-of-training-sample and
bias-to-supervised-sample issues can be evidently alleviated. Experiments on
synthetic and real data verify the superiority of our model compared to the
state-of-the-arts.
