Rain effect in images typically is annoying for many multimedia and computer
vision tasks. For removing rain effect from a single image, deep leaning
techniques have been attracting considerable attentions. This paper designs a
novel multi-task leaning architecture in an end-to-end manner to reduce the
mapping range from input to output and boost the performance. Concretely, a
decomposition net is built to split rain images into clean background and rain
layers. Different from previous architectures, our model consists of, besides a
component representing the desired clean image, an extra component for the rain
layer. During the training phase, we further employ a composition structure to
reproduce the input by the separated clean image and rain information for
improving the quality of decomposition. Experimental results on both synthetic
and real images are conducted to reveal the high-quality recovery by our
design, and show its superiority over other state-of-the-art methods.
Furthermore, our design is also applicable to other layer decomposition tasks
like dust removal. More importantly, our method only requires about 50ms,
significantly faster than the competitors, to process a testing image in VGA
resolution on a GTX 1080 GPU, making it attractive for practical use.

本文设计了一种新的多任务深度学习体系结构，通过分解网络将雨图像分为干净的背景和雨层，进而进行去雨处理和图像恢复。实验结果表明，该结构在速度快、效果好方面表现优秀，同时具有广泛的应用前景。