Harnessing Joint Rain-/Detail-aware Representations to Eliminate
  Intricate Rains
Wu Ran Peirong Ma Zhiquan He Hao Ren Hong Lu
Recent advances in image deraining have focused on training powerful models
on mixed multiple datasets comprising diverse rain types and backgrounds.
However, this approach tends to overlook the inherent differences among rainy
images, leading to suboptimal results. To overcome this limitation, we focus on
addressing various rainy images by delving into meaningful representations that
encapsulate both the rain and background components. Leveraging these
representations as instructive guidance, we put forth a Context-based
Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating
CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware
contrastive learning strategy to help extract joint rain-/detail-aware
representations. By integrating CoI-M with the rain-/detail-aware Contrastive
learning, we develop CoIC, an innovative and potent algorithm tailored for
training models on mixed datasets. Moreover, CoIC offers insight into modeling
relationships of datasets, quantitatively assessing the impact of rain and
details on restoration, and unveiling distinct behaviors of models given
diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting
the deraining ability of CNN and Transformer models. CoIC also enhances the
deraining prowess remarkably when real-world dataset is included.