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%2C recent unsupervised methods have achieved notable success. However%2C these methods often struggle to generate high-quality%2C rain-free images due to a lack of attention to semantic representation and image content%2C resulting in ineffective separation of content from the rain layer. In this paper%2C we propose a novel cycle contrastive generative adversarial network for unsupervised SID%2C called CCLGAN. This framework combines cycle contrastive learning (CCL) and location contrastive learning (LCL). CCL improves image reconstruction and rain-layer removal by bringing similar features closer and pushing dissimilar features apart in both semantic and discriminative spaces. At the same time%2C LCL preserves content information by constraining mutual information at the same location across different exemplars. CCLGAN shows superior performance%2C as extensive experiments demonstrate the benefits of CCLGAN and the effectiveness of its components.?quality=80&w=800)
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