  We present a novel direction-aware feature-level frequency decomposition
network for single image deraining. Compared with existing solutions, the
proposed network has three compelling characteristics. First, unlike previous
algorithms, we propose to perform frequency decomposition at feature-level
instead of image-level, allowing both low-frequency maps containing structures
and high-frequency maps containing details to be continuously refined during
the training procedure. Second, we further establish communication channels
between low-frequency maps and high-frequency maps to interactively capture
structures from high-frequency maps and add them back to low-frequency maps
and, simultaneously, extract details from low-frequency maps and send them back
to high-frequency maps, thereby removing rain streaks while preserving more
delicate features in the input image. Third, different from existing algorithms
using convolutional filters consistent in all directions, we propose a
direction-aware filter to capture the direction of rain streaks in order to
more effectively and thoroughly purge the input images of rain streaks. We
extensively evaluate the proposed approach in three representative datasets and
experimental results corroborate our approach consistently outperforms
state-of-the-art deraining algorithms.
