It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. Although the CNN based methods have reported promising performance recently, there are still some defects, such as data dependency and insufficient interpretation. A single image deraining algorithm based on the combination of data-driven and model-based approaches is proposed. Firstly, an improved weighted guided image filter (iWGIF) is used to extract high-frequency information and learn the rain steaks to avoid interference from other information through the input image. Then, transfering the input image and rain steaks from the image domain to the feature domain adaptively to learn useful features for high-quality image deraining. Finally, networks with attention mechanisms is used to restore high-quality images from the latent features. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both qualitative and quantitative measures.

翻译：单幅雨图去除雨痕是一个具有挑战性的任务，因为雨痕在空间上呈现非均匀分布。尽管基于卷积神经网络的方法近年来取得了令人瞩目的性能，但仍存在数据依赖性强和解释性不足等缺陷。本文提出一种融合数据驱动与模型驱动方法的单幅图像去雨算法。首先，采用改进加权引导图像滤波器提取高频信息，通过输入图像学习雨痕特征以避免其他信息的干扰；其次，将输入图像与雨痕从图像域自适应迁移至特征域，以学习高质量图像去雨所需的有效特征；最后，基于注意力机制的网络被用于从潜在特征重建高质量图像。实验结果表明，该算法在定性和定量指标上均显著优于现有最优方法。