In real-world environments, outdoor imaging systems are often affected by
disturbances such as rain degradation. Especially, in nighttime driving scenes,
insufficient and uneven lighting shrouds the scenes in darkness, resulting
degradation of both the image quality and visibility. Particularly, in the
field of autonomous driving, the visual perception ability of RGB sensors
experiences a sharp decline in such harsh scenarios. Additionally, driving
assistance systems suffer from reduced capabilities in capturing and discerning
the surrounding environment, posing a threat to driving safety. Single-view
information captured by single-modal sensors cannot comprehensively depict the
entire scene. To address these challenges, we developed an image de-raining
framework tailored for rainy nighttime driving scenes. It aims to remove rain
artifacts, enrich scene representation, and restore useful information.
Specifically, we introduce cooperative learning between visible and infrared
images captured by different sensors. By cross-view fusion of these
multi-source data, the scene within the images gains richer texture details and
enhanced contrast. We constructed an information cleaning module called
CleanNet as the first stage of our framework. Moreover, we designed an
information fusion module called FusionNet as the second stage to fuse the
clean visible images with infrared images. Using this stage-by-stage learning
strategy, we obtain de-rained fusion images with higher quality and better
visual perception. Extensive experiments demonstrate the effectiveness of our
proposed Cross-View Cooperative Learning (CVCL) in adverse driving scenarios in
low-light rainy environments. The proposed approach addresses the gap in the
utilization of existing rain removal algorithms in specific low-light
conditions.

针对夜间行驶场景中受雨水降解影响、图像质量和能见度下降的问题，我们开发了一个夜间行驶场景下的图像去雨框架，通过可见光和红外图像的跨视图融合，去除雨痕、丰富场景表征并恢复有用信息，实验证明了我们提出的跨视图合作学习方法在低光强雨水环境下具有较好效果，并弥补了现有去雨算法在特定低光条件下的利用缺口。