Rain streaks significantly decrease the visibility of captured images and are also a stumbling block that restricts the performance of subsequent computer vision applications. The existing deep learning-based image deraining methods employ manually crafted networks and learn a straightforward projection from rainy images to clear images. In pursuit of better deraining performance, they focus on elaborating a more complicated architecture rather than exploiting the intrinsic properties of the positive and negative information. In this paper, we propose a contrastive learning-based image deraining method that investigates the correlation between rainy and clear images and leverages a contrastive prior to optimize the mutual information of the rainy and restored counterparts. Given the complex and varied real-world rain patterns, we develop a recursive mechanism. It involves multi-scale feature extraction and dynamic cross-level information recruitment modules. The former advances the portrayal of diverse rain patterns more precisely, while the latter can selectively compensate high-level features for shallow-level information. We term the proposed recursive dynamic multi-scale network with a contrastive prior, RDMC. Extensive experiments on synthetic benchmarks and real-world images demonstrate that the proposed RDMC delivers strong performance on the depiction of rain streaks and outperforms the state-of-the-art methods. Moreover, a practical evaluation of object detection and semantic segmentation shows the effectiveness of the proposed method.

翻译：雨线显著降低了拍摄图像的可见性，并且是限制后续计算机视觉应用性能的障碍。现有的基于深度学习的图像去雨方法采用人工设计的网络，学习从雨图到清晰图像的简单映射。为了追求更好的去雨性能，这些方法专注于设计更复杂的架构，而非利用正负信息的内在特性。本文提出了一种基于对比学习的图像去雨方法，该方法研究了雨图和清晰图像之间的相关性，并利用对比先验来优化雨图与恢复图像之间的互信息。针对现实世界中复杂多变的雨纹模式，我们开发了一种递归机制，其中包含多尺度特征提取模块和动态跨层级信息招募模块。前者能够更精确地刻画多样化雨纹模式，后者则可选择性地将高层特征补充至浅层信息。我们将所提出的带有对比先验的递归动态多尺度网络命名为RDMC。在合成基准和真实图像上的大量实验表明，所提出的RDMC在雨线刻画方面表现出强大性能，并优于当前最先进方法。此外，针对目标检测和语义分割的实际评估验证了所提方法的有效性。