  Removing the rain streaks from single image is still a challenging task,
since the shapes and directions of rain streaks in the synthetic datasets are
very different from real images. Although supervised deep deraining networks
have obtained impressive results on synthetic datasets, they still cannot
obtain satisfactory results on real images due to weak generalization of rain
removal capacity, i.e., the pre-trained models usually cannot handle new shapes
and directions that may lead to over-derained/under-derained results. In this
paper, we propose a new semi-supervised GAN-based deraining network termed
Semi-DerainGAN, which can use both synthetic and real rainy images in a uniform
network using two supervised and unsupervised processes. Specifically, a
semi-supervised rain streak learner termed SSRML sharing the same parameters of
both processes is derived, which makes the real images contribute more rain
streak information. To deliver better deraining results, we design a paired
discriminator for distinguishing the real pairs from fake pairs. Note that we
also contribute a new real-world rainy image dataset Real200 to alleviate the
difference between the synthetic and real image do-mains. Extensive results on
public datasets show that our model can obtain competitive performance,
especially on real images.
