The quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining L₁ loss and edge loss is used to guide the training process to pay attention to edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.

照片的质量极易受到恶劣天气（例如大雨）的影响；它也会降低诸如对象检测之类的各种视觉任务的性能。去除雨水是一个具有挑战性的问题，因为即使在一张图像中，雨水条纹也具有不同的外观。雨水积聚的地区似乎有雾或薄雾，而在雨量较少的地区则可以清楚地看到雨水条纹。我们建议使用混合多尺度损失导向的多特征融合排水网络（MSGMFFNet）消除图片中的各种降雨效果。特别地，为了处理雨水条纹，我们的方法生成了雨水条纹注意图，而预处理使用伽玛校正和对比度增强来增强图像以解决雨水积累问题。使用这些工具，模型可以还原具有大量细节的结果。此外，L ₁丢失和边缘丢失用于指导训练过程注意边缘和内容信息。在合成数据集和真实数据集上进行的综合实验证明了我们方法的有效性。