Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density. This varying characteristic of rain streaks affect different parts of the image differently. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. We extend our previous work UMRL network, and propose Image Quality-based single image Deraining using Confidence measure (QuDeC), network addresses this issue by learning the quality or distortion level of each patch in the rainy image, and further processes this information to learn the rain content at different scales. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate of both quality at each location and residual rain streak information (residual map). Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods.

中文翻译：

---

置信测度指导的单图像降噪

单图像去雨是一个极具挑战性的问题，因为多雨图像包含雨条纹，雨条纹通常在大小，方向和密度上有所不同。雨水条纹的这种变化特征对图像的不同部分产生不同的影响。先前的方法已经尝试通过利用一些先前的信息来从单个图像中去除雨水条纹来解决该问题。这些方法的主要限制之一是它们没有考虑图像中雨滴的位置信息。我们扩展了先前的UMRL网络，并提出了基于图像质量的基于置信度测度（QuDeC）的单幅图像消除，网络通过学习多雨图像中每个色块的质量或失真程度来解决此问题，并进一步处理该信息以学习不同规模的雨量。此外，我们介绍了一种技术，该技术基于有关每个位置的质量估计值和残余雨条纹信息（残余图）的置信度度量，来指导网络学习网络权重。在合成数据集和真实数据集上的大量实验表明，与最近的最新技术方法相比，该方法取得了显着改进。