  Removal of rain streaks from a single image is an extremely challenging
problem since the rainy images often contain rain streaks of different size,
shape, direction and density. Most recent methods for deraining use a deep
network following a generic "encoder-decoder" architecture which captures
low-level features across the initial layers and high-level features in the
deeper layers. For the task of deraining, the rain streaks which are to be
removed are relatively small and focusing much on global features is not an
efficient way to solve the problem. To this end, we propose using an
overcomplete convolutional network architecture which gives special attention
in learning local structures by restraining the receptive field of filters. We
combine it with U-Net so that it does not lose out on the global structures as
well while focusing more on low-level features, to compute the derained image.
The proposed network called, Over-and-Under Complete Deraining Network (OUCD),
consists of two branches: overcomplete branch which is confined to small
receptive field size in order to focus on the local structures and an
undercomplete branch that has larger receptive fields to primarily focus on
global structures. Extensive experiments on synthetic and real datasets
demonstrate that the proposed method achieves significant improvements over the
recent state-of-the-art methods.
