Lightweight single image deraining algorithm incorporating visual saliency

Abstract

Deep learning (DL) methods have achieved excellent performance in the task of single image rain removal, however, there are still some challenges, such as artifact remnant, background over-smooth, and more and more complex and heavy-weight network architecture. Due to too heavy-weight network to suit outdoor detection devices or mobile devices, therefore, we propose a light-weight single image deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The network consists of dilation convolution module, the convolutional block attention module (CBAM), and gated recursive unit module. Specifically, rain steaks feature maps are extracted by the combinations of dilated convolution with CBAM, which also facilitates accurate localisation of rain steak location, and then the three gated recursive units is cascaded to remove steaks stage by stage. The dilated convolution module and CBAM are used to reduce network’s weight size and retain the rain removal result, thus our LDVS method belongs to the lightweight with only 50703 parameters. Extensive experiments on synthetic and real-world datasets have demonstrated that our method outperforms the baseline both under qualitative and quantitative analysis. Under the same rain removal result, our method is less time cost and less burden.