The visual quality of images captured in rainy weather is degraded by rain streaks, which may significantly reduce the accuracy of computer vision systems. In this paper, an internal and external transmission encoder–decoder network is proposed for eliminating rain streaks in a single image. Since rain streaks and background structures tend to be spatially long, we apply an internal connected aggregation unit to leverage global information and aggregate features at different scales to help remove rain streaks while restoring scene details. To fully exploit effective features of the encoder network and remove redundant information, we utilize an external connected enhancement unit to obtain effective feature maps for predicting clear outputs. By utilizing the efficient information inside and outside the unit, the proposed method can remove the rain streaks of different sizes and directions in the image and restore clear background structure details. Extensive experimental results on synthetic datasets and natural samples show that our method can achieve better rain removal performance than other advanced methods.

雨天拍摄的图像的视觉质量会因雨纹而降低，这可能会显着降低计算机视觉系统的准确性。在本文中，提出了一种内部和外部传输编码器-解码器网络来消除单个图像中的雨纹。由于雨条纹和背景结构往往在空间上很长，因此我们应用内部连接的聚合单元来利用全局信息并聚合不同尺度的特征，以帮助消除雨条纹，同时恢复场景细节。为了充分利用编码器网络的有效特征并去除冗余信息，我们利用外部连接的增强单元来获得有效的特征图以预测清晰的输出。该方法利用单元内外的有效信息，可以去除图像中不同大小和方向的雨纹，恢复清晰的背景结构细节。对合成数据集和自然样本的大量实验结果表明，我们的方法比其他先进方法可以获得更好的除雨性能。