Single image rain removal via densely connected contextual and semantic correlation net

Abstract

Rainy images severely degrade visibility. Thus, deraining is an important task for applications ranging from image processing to computer vision. We propose a deep learning-based method to remove rain streaks from a single image. Specifically, we first design a deraining unit that employs dilation convolution and squeeze-and-excitation operations, respectively, to obtain more spatial contextual information and semantic correlation. In the deraining unit, multifeatures at different levels can be obtained by using convolutions with different dilation factors, and they are fused to maintain the primary features of rain streaks. Then, we interconnect the deraining units by dense connections that can maximize the information flow along features from different levels and make them be associated. Both deraining units and dense connections make our network have stronger representative ability of the rain streaks layer. Experimental results show that our proposed deraining method outperforms state-of-the-art methods by a good margin in Rain100H, Rain100L, and Rain1200 datasets, while using fewer parameters.