The capability of image deraining is a highly desirable component of intelligent decision-making in autonomous driving and outdoor surveillance systems. Image deraining aims to restore the clean scene from the degraded image captured in a rainy day. Although numerous single image deraining algorithms have been recently proposed, these algorithms are mainly evaluated using certain type of synthetic images, assuming a specific rain model, plus a few real images. It remains unclear how these algorithms would perform on rainy images acquired “in the wild” and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images of various rain types. This dataset highlights diverse rain models (rain streak, rain drop, rain and mist), as well as a rich variety of evaluation criteria (full- and no-reference objective, subjective, and task-specific). We further provide a comprehensive suite of criteria for deraining algorithm evaluation, including full- and no-reference metrics, subjective evaluation, and the novel task-driven evaluation. The proposed benchmark is accompanied with extensive experimental results that facilitate the assessment of the state-of-the-arts on a quantitative basis. Our evaluation and analysis indicate the gap between the achievable performance on synthetic rainy images and the practical demand on real-world images. We show that, despite many advances, image deraining is still a largely open problem. The paper is concluded by summarizing our general observations, identifying open research challenges and pointing out future directions. Our code and dataset is publicly available at http://uee.me/ddQsw.

图像清除能力是自动驾驶和户外监视系统中智能决策的非常理想的组件。图像清除旨在从雨天捕获的降级图像中恢复干净的场景。尽管最近已经提出了许多单图像清除算法，但这些算法主要是使用特定类型的合成图像（假设特定的降雨模型以及一些实际图像）进行评估的。尚不清楚这些算法将如何在“野外”获得的多雨图像上执行，以及我们如何评估该领域的进展。本文旨在弥合这一差距。我们使用新的大规模基准测试，对现有的单个图像去除算法进行了全面的研究和评估，该基准包含各种降雨类型的合成和真实世界的降雨图像。该数据集重点介绍了多种降雨模型（降雨条带，雨滴，雨水和薄雾）以及多种评估标准（全面参考和无参考目标，主观和特定任务）。我们进一步提供了一套完善的算法评估标准，其中包括全参考和无参考指标，主观评估以及新颖的任务驱动评估。拟议的基准测试伴随着广泛的实验结果，有助于定量评估最新技术水平。我们的评估和分析表明，合成雨天图像可实现的性能与现实世界图像的实际需求之间存在差距。我们表明，尽管取得了许多进步，但是图像排水仍然是一个很大的开放问题。本文是通过总结我们的一般性结论得出的结论，找出开放的研究挑战并指出未来的方向。我们的代码和数据集可从http://uee.me/ddQsw上公开获得。