  Existing deep convolutional neural networks have found major success in image
deraining, but at the expense of an enormous number of parameters. This limits
their potential application, for example in mobile devices. In this paper, we
propose a lightweight pyramid of networks (LPNet) for single image deraining.
Instead of designing a complex network structures, we use domain-specific
knowledge to simplify the learning process. Specifically, we find that by
introducing the mature Gaussian-Laplacian image pyramid decomposition
technology to the neural network, the learning problem at each pyramid level is
greatly simplified and can be handled by a relatively shallow network with few
parameters. We adopt recursive and residual network structures to build the
proposed LPNet, which has less than 8K parameters while still achieving
state-of-the-art performance on rain removal. We also discuss the potential
value of LPNet for other low- and high-level vision tasks.
