Recent years have witnessed significant advances in image deraining due to the kinds of effective image priors and deep learning models. As each deraining approach has individual settings (e.g., training and test datasets, evaluation criteria), how to fairly evaluate existing approaches comprehensively is not a trivial task. Although existing surveys aim to review of image deraining approaches comprehensively, few of them focus on providing unify evaluation settings to examine the deraining capability and practicality evaluation. In this paper, we provide a comprehensive review of existing image deraining method and provide a unify evaluation setting to evaluate the performance of image deraining methods. We construct a new high-quality benchmark named HQ-RAIN to further conduct extensive evaluation, consisting of 5,000 paired high-resolution synthetic images with higher harmony and realism. We also discuss the existing challenges and highlight several future research opportunities worth exploring. To facilitate the reproduction and tracking of the latest deraining technologies for general users, we build an online platform to provide the off-the-shelf toolkit, involving the large-scale performance evaluation. This online platform and the proposed new benchmark are publicly available and will be regularly updated at http://www.deraining.tech/.

翻译：近年来，由于各种有效的图像先验和深度学习模型，图像去雨领域取得了显著进展。由于每种去雨方法都有其独立的设置（例如训练和测试数据集、评估标准），如何公平全面地评估现有方法并非易事。尽管现有综述旨在全面回顾图像去雨方法，但鲜有研究致力于提供统一的评估设置来检验去雨能力及实用性评估。本文对现有图像去雨方法进行了全面综述，并提出了统一的评估设置以评测图像去雨方法的性能。我们构建了一个名为HQ-RAIN的新高质量基准，包含5000对具有更高和谐度与真实感的高分辨率合成图像，以开展广泛评估。此外，我们探讨了现有挑战，并指出了若干值得探索的未来研究方向。为方便普通用户复现和追踪最新去雨技术，我们搭建了一个在线平台，提供包含大规模性能评估的即用型工具包。该在线平台及所提出的新基准已公开发布，并将在http://www.deraining.tech/定期更新。