  We propose a simple yet effective deep tree-structured fusion model based on
feature aggregation for the deraining problem. We argue that by effectively
aggregating features, a relatively simple network can still handle tough image
deraining problems well. First, to capture the spatial structure of rain we use
dilated convolutions as our basic network block. We then design a
tree-structured fusion architecture which is deployed within each block
(spatial information) and across all blocks (content information). Our method
is based on the assumption that adjacent features contain redundant
information. This redundancy obstructs generation of new representations and
can be reduced by hierarchically fusing adjacent features. Thus, the proposed
model is more compact and can effectively use spatial and content information.
Experiments on synthetic and real-world datasets show that our network achieves
better deraining results with fewer parameters.
