  Rain removal in images is an important task in computer vision filed and
attracting attentions of more and more people. In this paper, we address a
non-trivial issue of removing visual effect of rain streak from a single image.
Differing from existing work, our method combines various semantic constraint
task in a proposed multi-task regression model for rain removal. These tasks
reinforce the model's capabilities from the content, edge-aware, and local
texture similarity respectively. To further improve the performance of
multi-task learning, we also present two simple but powerful dynamic weighting
algorithms. The proposed multi-task enhanced network (MENET) is a powerful
convolutional neural network based on U-Net for rain removal research, with a
specific focus on utilize multiple tasks constraints and exploit the synergy
among them to facilitate the model's rain removal capacity. It is noteworthy
that the adaptive weighting scheme has further resulted in improved network
capability. We conduct several experiments on synthetic and real rain images,
and achieve superior rain removal performance over several selected
state-of-the-art (SOTA) approaches. The overall effect of our method is
impressive, even in the decomposition of heavy rain and rain streak
accumulation.The source code and some results can be found
at:https://github.com/SumiHui/MENET.
