Recently, rain streaks removal from a single image has attracted much research attention to alleviate the degenerated performance of computer vision tasks implemented on rainy images. In this paper, we provide a thorough review for current single-image-based rain removal techniques, which can be mainly categorized into three classes: early filter-based, conventional prior-based, and recent deep learning-based approaches. Furthermore, inspired by the rationality of current deep learning-based methods and insightful characteristics underlying rain shapes, we build a specific coarse-to-fine deraining network architecture, which can finely deliver the rain structures and progressively removes rain streaks from the input image, accordingly. The superiority of the proposed network is substantiated by experiments implemented on synthetic and real rainy images both visually and quantitatively, as compared with comprehensive state-of-the-art methods along this line. Especially, it is verified that the proposed network possesses better generalization capability on real rainy images, implying its potential usefulness for this task.

中文翻译：

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去除单个图像的雨水条纹：回顾和探索

近来，从单个图像上去除雨纹已经引起许多研究关注，以减轻在多雨图像上执行的计算机视觉任务的退化性能。在本文中，我们对当前基于单图像的除雨技术进行了全面的回顾，该技术主要可分为三类：基于早期过滤器，常规基于先验和最近基于深度学习的方法。此外，受当前基于深度学习的方法的合理性和降雨形状的深刻见解的启发，我们构建了一种特定的从粗到细的排水网络架构，该架构可以精细地传递降雨结构并逐渐从输入图像中消除降雨条纹，相应地。与沿这条线综合的最新技术相比，拟议网络的优越性在视觉和定量上对合成和真实的多雨图像进行的实验得以证实。特别是，可以证明所提出的网络在实际的雨天图像上具有更好的泛化能力，这暗示了其对这项任务的潜在实用性。