Single-image deraining is a classical problem in the field of low-level computer tasks. Most of the recent state-of-the-art image rain removal methods are trained on synthetic images, which have the problems of incomplete rain removal on real images and inability to process complex rain conditions. Based on these limitations, we propose a single-image deraining network based on multistage feature fusion (SIDNMFF). The network performs rain removal in four stages, with the first three stages using an improved encoder–decoder subnetwork for feature fusion to extract global and local information from the image, resulting in more detailed texture information of the obtained image. In the last stage, the network fuses the feature information extracted in the first three stages, performs feature extraction on the original resolution image, and finally outputs the final result of multistage deraining. The proposed method conducts a large number of comparative experiments on synthetic and real datasets as well as experiments on real datasets using no-reference metrics and the target detection method as the evaluation basis. Experimental results confirm that the proposed method achieves a satisfactory rain removal effect and outperforms the other methods.

中文翻译：

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基于多级特征融合的单幅图像去雨网络

单图像去雨是低级计算机任务领域的经典问题。近期最先进的图像去雨方法大多是在合成图像上训练的，存在对真实图像去雨不完全、无法处理复杂雨况的问题。基于这些限制，我们提出了一种基于多级特征融合（SIDNMFF）的单图像去雨网络。该网络分四个阶段执行去雨，前三个阶段使用改进的编码器-解码器子网络进行特征融合，从图像中提取全局和局部信息，从而获得更详细的图像纹理信息。最后阶段，网络融合前三个阶段提取的特征信息，对原始分辨率图像进行特征提取，最后输出多级去雨的最终结果。所提出的方法在合成数据集和真实数据集上进行了大量的对比实验，以及在真实数据集上使用无参考指标和目标检测方法作为评估基础的实验。实验结果证实，该方法取得了令人满意的除雨效果，优于其他方法。