Aiming at the problem that the encoder-decoder structure easily leads to the lack of texture details of the image,and the global semantic information of the image processed by the single-scale structure is not accurate e-nough,a Multi-stage and Multi-scale Deraining Network(MMDNet)is designed.Specifically,an effective feature at-tention module is firstly proposed,which is used to form an encoder-decoder and a single-scale module to learn the features of rain streaks at different scales;In addition,a semantic feature fusion module is introduced to fuse the fea-tures of the encoder and decoder,and the rich semantic information is passed to the next stage;Furthermore,an infor-mation transition module is designed between the stages of the network,which on the one hand transitions the shallow information to the next stage,and on the other hand plays a supervisory role.Comprehensive experiments demonstrate that the performance of the proposed algorithm is competitive with other state-of-the-art rain removal algorithms on the Rain100H,Rain100L and Test100 datasets.
NETL
NSTL
万方数据
