  Images captured under complicated rain conditions often suffer from
noticeable degradation of visibility. The rain models generally introduce
diversity visibility degradation, which includes rain streak, rain drop as well
as rain mist. Numerous existing single image deraining methods focus on the
only one type rain model, which does not have strong generalization ability. In
this paper, we propose a novel end-to-end Neuron Attention Stage-by-Stage Net
(NASNet), which can solve all types of rain model tasks efficiently. For one
thing, we pay more attention on the Neuron relationship and propose a
lightweight Neuron Attention (NA) architectural mechanism. It can adaptively
recalibrate neuron-wise feature responses by modelling interdependencies and
mutual influence between neurons. Our NA architecture consists of Depthwise
Conv and Pointwise Conv, which has slight computation cost and higher
performance than SE block by our contrasted experiments. For another, we
propose a stage-by-stage unified pattern network architecture, the
stage-by-stage strategy guides the later stage by incorporating the useful
information in previous stage. We concatenate and fuse stage-level information
dynamically by NA module. Extensive experiments demonstrate that our proposed
NASNet significantly outperforms the state-of-theart methods by a large margin
in terms of both quantitative and qualitative measures on all six public
large-scale datasets for three rain model tasks.
