  Rain streaks bring serious blurring and visual quality degradation, which
often vary in size, direction and density. Current CNN-based methods achieve
encouraging performance, while are limited to depict rain characteristics and
recover image details in the poor visibility environment. To address these
issues, we present a Multi-scale Hourglass Hierarchical Fusion Network
(MH2F-Net) in end-to-end manner, to exactly captures rain streak features with
multi-scale extraction, hierarchical distillation and information aggregation.
For better extracting the features, a novel Multi-scale Hourglass Extraction
Block (MHEB) is proposed to get local and global features across different
scales through down- and up-sample process. Besides, a Hierarchical Attentive
Distillation Block (HADB) then employs the dual attention feature responses to
adaptively recalibrate the hierarchical features and eliminate the redundant
ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy
to progressively discriminate feature learning and aggregate different features
instead of directly concatenating or adding. Extensive experiments on both
synthetic and real rainy datasets demonstrate the effectiveness of the designed
MH2F-Net by comparing with recent state-of-the-art deraining algorithms. Our
source code will be available on the GitHub:
https://github.com/cxtalk/MH2F-Net.
