  Rain streaks in the air appear in various blurring degrees and resolutions
due to different distances from their positions to the camera. Similar rain
patterns are visible in a rain image as well as its multi-scale (or
multi-resolution) versions, which makes it possible to exploit such
complementary information for rain streak representation. In this work, we
explore the multi-scale collaborative representation for rain streaks from the
perspective of input image scales and hierarchical deep features in a unified
framework, termed multi-scale progressive fusion network (MSPFN) for single
image rain streak removal. For similar rain streaks at different positions, we
employ recurrent calculation to capture the global texture, thus allowing to
explore the complementary and redundant information at the spatial dimension to
characterize target rain streaks. Besides, we construct multi-scale pyramid
structure, and further introduce the attention mechanism to guide the fine
fusion of this correlated information from different scales. This multi-scale
progressive fusion strategy not only promotes the cooperative representation,
but also boosts the end-to-end training. Our proposed method is extensively
evaluated on several benchmark datasets and achieves state-of-the-art results.
Moreover, we conduct experiments on joint deraining, detection, and
segmentation tasks, and inspire a new research direction of vision task-driven
image deraining. The source code is available at
\url{https://github.com/kuihua/MSPFN}.
