Contrastive Learning with Weakly Pair Images for Traffic Image Deraining

Image deraining is an image restoration task that aims at decomposing a rainy image into the clear scene layer and the rain layer. Most existing deraining methods use supervised learning and training on synthetic rainy-clear pairs under the assumption that the only difference between rain and non-rain images is the rain effect. Since scenes constantly change due to vehicle movement and other activities, this assumption nearly fails when applied to traffic data. In this work, we introduce a de-raining framework using contrastive learning based on Vision Transformer. The proposed framework utilizes weakly paired images, in which the difference between images is not only caused by rain. In addition, we collect a real-world deraining dataset from traffic surveillance cameras. This is the first real-world deraining dataset in the traffic domain. The experiments show that our method achieves competitive performance with other existing methods while taking less time in the inference process.