Removing rain streaks from single images is an important problem in various
computer vision tasks because rain streaks can degrade outdoor images and
reduce their visibility. While recent convolutional neural network-based
deraining models have succeeded in capturing rain streaks effectively,
difficulties in recovering the details in rain-free images still remain. In
this paper, we present a multi-level connection and adaptive regional attention
network (MARA-Net) to properly restore the original background textures in
rainy images. The first main idea is a multi-level connection design that
repeatedly connects multi-level features of the encoder network to the decoder
network. Multi-level connections encourage the decoding process to use the
feature information of all levels. Channel attention is considered in
multi-level connections to learn which level of features is important in the
decoding process of the current level. The second main idea is a wide regional
non-local block (WRNL). 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