In reality, rain and fog are often present at the same time, which can greatly reduce the clarity and quality of the scene image. However, most unsupervised single image deraining methods mainly focus on rain streak removal by disregarding the fog, which leads to low-quality deraining performance. In addition, the samples are rather homogeneous generated by these methods and lack diversity, resulting in poor results in the face of complex rain scenes. To address the above issues, we propose a novel Asymetric Cycle Generative and Adversarial framework (ACGF) for single image deraining that trains on both synthetic and real rainy images while simultaneously capturing both rain streaks and fog features. ACGF consists of a Rain-fog2Clean (R2C) transformation block and a Clean2Rain-fog (C2R) transformation block. The former consists of parallel rain removal path and rain-fog feature extraction path by the rain and derain-fog network and the attention rain-fog feature extraction network (ARFE) , while the latter only contains a synthetic rain transformation path. In rain-fog feature extraction path, to better characterize the rain-fog fusion feature, we employ an ARFE to exploit the self-similarity of global and local rain-fog information by learning the spatial feature correlations. Moreover, to improve the translational capacity of C2R and the diversity of models, we design a rain-fog feature decoupling and reorganization network (RFDR) by embedding a rainy image degradation model and a mixed discriminator to preserve richer texture details in synthetic rain conversion path. Extensive experiments on benchmark rain-fog and rain datasets show that ACGF outperforms state-of-the-art deraining methods. We also conduct defogging performance evaluation experiments to further demonstrate the effectiveness of ACGF.

翻译：现实中，雨和雾常常同时存在，这会大大降低场景图像的清晰度和质量。然而，大多数无监督单幅图像去雨方法主要关注去除雨纹，而忽略了雾气，导致去雨性能不佳。此外，这些方法生成的样本较为单一且缺乏多样性，导致在面对复杂雨景时效果不佳。为了解决上述问题，我们提出了一种新颖的非对称循环生成对抗框架（ACGF），用于单幅图像去雨，该框架同时在合成和真实雨图上进行训练，并同时捕捉雨纹和雾气特征。ACGF由雨雾转干净（R2C）变换块和干净转雨雾（C2R）变换块组成。前者通过雨雾去除网络和注意力雨雾特征提取网络（ARFE）包含并行去雨路径和雨雾特征提取路径，而后者仅包含一个合成雨变换路径。在雨雾特征提取路径中，为了更好地刻画雨雾融合特征，我们采用ARFE通过学习空间特征相关性来挖掘全局和局部雨雾信息的自相似性。此外，为了提升C2R的变换能力和模型的多样性，我们通过嵌入雨图退化模型和混合判别器，设计了一个雨雾特征解耦与重组网络（RFDR），以在合成雨变换路径中保留更丰富的纹理细节。在基准雨雾和雨图数据集上进行的大量实验表明，ACGF优于当前最先进的去雨方法。我们还进行了去雾性能评估实验，进一步证明了ACGF的有效性。