Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining

Understand

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.