Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops. Inside the DAM, there are two attentive maps - each of which attends to the heavy and light rainy regions, respectively, to guide the deraining process differently for applicable regions. In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a “heavy-to-light” scheme to remove rain via addressing the unsatisfying deraining regions. Extensive experiments on one public raindrop dataset, one public rain streak and our synthesized joint rain streak and raindrop (JRSRD) dataset have demonstrated that the proposed method not only is capable of removing rain streaks and raindrops simultaneously, but also achieves the state-of-the-art performance on both tasks.

中文翻译：

---

联合雨条纹和雨滴去除的双重注意力模型

雨痕和雨滴是两种自然现象，它们会以不同的方式降低图像捕获能力。目前，大多数现有的深度排水网络将它们视为两个不同的问题并单独解决一个问题，因此无法同时充分处理两者。为了解决这个问题，我们提出了一个双重注意力模型（DAiAM），它包括两个用于去除雨条纹和雨滴的 DAM。在 DAM 内部，有两个细心的地图 - 每个地图分别关注大雨和小雨区域，以针对适用区域以不同的方式引导排水过程。此外，为了进一步完善结果，提出了一种差分驱动的双重注意力模型（D-DAiAM），采用“从重到轻”的方案，通过解决不满意的排水区域来去除雨水。