Semi-Swinderain: Semi-Supervised Image Deraining Network Using SWIN Transformer

Abstract

Currently, single image deraining lacks paired rain/clean images in real world and most studies use synthetic data. Real rain image deraining is still a challenge. To solve this problem, we propose a semi-supervised image deraining network using Swin Transformer, which can both use features of synthetic data and real data to get a better result. Specifically, the network is divided into supervised branch and unsupervised branch. Supervised and unsupervised branches are trained using synthetic data and real data, respectively. The network architecture is based on Swin Transformer, which adds a self-supervised memory module between encoder and decoder to store rain information. In the unsupervised branch, contrastive loss is added to ensure restored real rain image in features space is close to clear image, away from real rain image. In addition, we propose a real rain dataset RealRain11k. Experiments show our method has better result in real rain image deraining. The source code and RealRain11k are available at https://github.com/imissrc/Semi-SwinDerain.