  Rain removal is an important but challenging computer vision task as rain
streaks can severely degrade the visibility of images that may make other
visions or multimedia tasks fail to work. Previous works mainly focused on
feature extraction and processing or neural network structure, while the
current rain removal methods can already achieve remarkable results, training
based on single network structure without considering the cross-scale
relationship may cause information drop-out. In this paper, we explore the
cross-scale manner between networks and inner-scale fusion operation to solve
the image rain removal task. Specifically, to learn features with different
scales, we propose a multi-sub-networks structure, where these sub-networks are
fused via a crossscale manner by Gate Recurrent Unit to inner-learn and make
full use of information at different scales in these sub-networks. Further, we
design an inner-scale connection block to utilize the multi-scale information
and features fusion way between different scales to improve rain representation
ability and we introduce the dense block with skip connection to inner-connect
these blocks. Experimental results on both synthetic and real-world datasets
have demonstrated the superiority of our proposed method, which outperforms
over the state-of-the-art methods. The source code will be available at
https://supercong94.wixsite.com/supercong94.
