  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.
