Image deraining is an extensively researched topic in low-level computer vision community. During the past, sufficient works have been proposed to address this problem. Though great improvements these methods have achieved, no derain network can confidently declare that it can solve rain removal problem perfectly. Single complex model may lead to overfitting, while simple model is too weak to achieve clear result. Therefore, in this paper, inspired from classic boosting idea, we have proposed an effective ensemble derain framework to aggregate multiple simple weak drain models to obtain a strong derain model. Cascade structural weighting-map are computed for adaptively emphasizing the quality of local derain regions. Struct-absolution losses are proposed to account for pixel-wise and local region-wise differences, and to facilitate embedding boosting idea into network training. The comprehensive experiments on public derain datasets and high-level vision tasks validate that our proposed model which just utilizes three generally weak derain subnets can achieve much better performance than compared state-of-the-art methods. Our ensemble framework has enough capacity that any state-of-the-art DL-based models can be taken as sub-modules to solve rain removal of multiple types within a single framework.

图像去雨是低级计算机视觉社区中广泛研究的主题。在过去，已经提出了足够的工作来解决这个问题。尽管这些方法取得了很大的改进，但没有一个去雨网络可以自信地宣称它可以完美地解决去雨问题。单一的复杂模型可能会导致过拟合，而简单的模型太弱而无法获得清晰的结果。因此，在本文中，受经典boosting思想的启发，我们提出了一个有效的集成去雨框架来聚合多个简单的弱排水模型以获得强去雨模型。计算级联结构加权图以自适应地强调局部排水区域的质量。建议结构免除损失来解释像素和局部区域的差异，并促进将 boosting 思想嵌入到网络训练中。公共 derain 数据集和高级视觉任务的综合实验验证，我们提出的模型仅利用三个通常较弱的 derain 子网可以获得比最先进的方法更好的性能。我们的集成框架具有足够的能力，任何最先进的基于深度学习的模型都可以作为子模块来解决单个框架内多种类型的雨水去除问题。