Rain Streaks in a single image can severely damage the visual quality, and thus degrade the performance of current computer vision algorithms. To remove the rain streaks effectively, plenty of CNN-based methods have recently been developed, and obtained impressive performance. However, most existing CNN-based methods focus on network design, while rarely exploits spatial correlations of feature. In this paper, we propose a deep self-attentive pyramid network (SAPN) for more powerful feature expression for single image de-raining. Specifically, we propose a self-attentive pyramid module (SAM), which consists of convolutional layers enhanced by self-attention calculation units (SACUs) to capture the abstraction of image contents, and deconvolutional layers to upsample the feature maps and recover image details. Besides, we propose self-attention based skip connections to symmetrically link convolutional and deconvolutional layers to exploit spatial contextual information better. To model rain streaks with various scales and shapes, a multi-scale pooling (MSP) module is also introduced to efficiently leverage features from different scales. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method in terms of both quantitative and visual quality.
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