  Rain streaks degrade the image quality and seriously affect the performance
of subsequent computer vision tasks, such as autonomous driving, social
security, etc. Therefore, removing rain streaks from a given rainy images is of
great significance. Convolutional neural networks(CNN) have been widely used in
image deraining tasks, however, the local computational characteristics of
convolutional operations limit the development of image deraining tasks.
Recently, the popular transformer has global computational features that can
further facilitate the development of image deraining tasks. In this paper, we
introduce Swin-transformer into the field of image deraining for the first time
to study the performance and potential of Swin-transformer in the field of
image deraining. Specifically, we improve the basic module of Swin-transformer
and design a three-branch model to implement single-image rain removal. The
former implements the basic rain pattern feature extraction, while the latter
fuses different features to further extract and process the image features. In
addition, we employ a jump connection to fuse deep features and shallow
features. In terms of experiments, the existing public dataset suffers from
image duplication and relatively homogeneous background. So we propose a new
dataset Rain3000 to validate our model. Therefore, we propose a new dataset
Rain3000 for validating our model. Experimental results on the publicly
available datasets Rain100L, Rain100H and our dataset Rain3000 show that our
proposed method has performance and inference speed advantages over the current
mainstream single-image rain streaks removal models.The source code will be
available at https://github.com/H-tfx/SDNet.
