Elemental Attention Mechanism-Guided Progressive Rain Removal Algorithm

De-rainy has become a pre-processing task for most computer vision systems. Combining recursive ideas to De-rainy models is currently popular. In this paper, the EAPRN model is proposed by introducing the elemental attention mechanism in the progressive residual network model. The elemental attention mainly consists of spatial attention and channel attention, which feature-weight the feature image in both spatial and channel dimensions and combine as elemental attention features. The introduction of elemental attention can help the model improve its fitness for the rain removal task, filter out important network layers and help the network process the rainy image. Experiments show that the EAPRN model has better visual results on different datasets and the quality of the De-rainy image is further improved.