Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity

Abstract

Most existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less generalized to complex real rainy scenes. Moreover, the existing methods mainly utilize the property of the image or rain layers independently, while few of them have considered their mutually exclusive relationship. To solve above dilemma, we explore the intrinsic intra-similarity within each layer and inter-exclusiveness between two layers and propose an unsupervised non-local contrastive learning (NLCL) deraining method. The non-local self-similarity image patches as the positives are tightly pulled together and rain patches as the negatives are remarkably pushed away, and vice versa. On one hand, the intrinsic self-similarity knowledge within positive/negative samples of each layer benefits us to discover more compact representation; on the other hand, the mutually exclusive property between the two layers enriches the discriminative decomposition. Thus, the internal self-similarity within each layer (similarity) and the external exclusive relationship of the two layers (dissimilarity) serving as a generic image prior jointly facilitate us to unsupervisedly differentiate the rain from clean image. We further discover that the intrinsic dimension of the non-local image patches is generally higher than that of the rain patches. This insight motivates us to design an asymmetric contrastive loss that precisely models the compactness discrepancy of the two layers, thereby improving the discriminative decomposition. In addition, recognizing the limited quality of existing real rain datasets, which are often small-scale or obtained from the internet, we collect a large-scale real dataset under various rainy weathers that contains high-resolution rainy images. Extensive experiments conducted on different real rainy datasets demonstrate that the proposed method obtains state-of-the-art performance in real deraining. Both the code and the newly collected datasets will be available at https://owuchangyuo.github.io.