  A recent line of convolutional neural network-based works has succeeded in
capturing rain streaks. However, difficulties in detailed recovery still
remain. In this paper, we present a multi-level connection and wide regional
non-local block network (MCW-Net) to properly restore the original background
textures in rainy images. Unlike existing encoder-decoder-based image deraining
models that improve performance with additional branches, MCW-Net improves
performance by maximizing information utilization without additional branches
through the following two proposed methods. The first method is a multi-level
connection that repeatedly connects multi-level features of the encoder network
to the decoder network. Multi-level connection encourages the decoding process
to use the feature information of all levels. In multi-level connection,
channel-wise attention is considered to learn which level of features is
important in the decoding process of the current level. The second method is a
wide regional non-local block. As rain streaks primarily exhibit a vertical
distribution, we divide the grid of the image into horizontally-wide patches
and apply a non-local operation to each region to explore the rich rain-free
background information. Experimental results on both synthetic and real-world
rainy datasets demonstrate that the proposed model significantly outperforms
existing state-of-the-art models. Furthermore, the results of the joint
deraining and segmentation experiment prove that our model contributes
effectively to other vision tasks.
