Many computer vision techniques infer properties of our physical world from images.
Although images are formed through the physics of light and mechanics, computer vision …

Abstract Single Image D eraining (SID) aims at recovering the rain-free background from an
image degraded by rain streaks. For the powerful fitting ability of deep neural networks and …

Level 5 autonomy for self-driving cars requires a robust visual perception system that can
parse input images under any visual condition. However, existing semantic segmentation …

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle
technology. Image based benchmark datasets have driven development in computer vision …

Abstract Domain adaptation is crucial to adapt a learned model to new scenarios, such as
domain shifts or changing data distributions. Current approaches usually require a large …

The goal of single-image deraining is to restore the rain-free background scenes of an
image degraded by rain streaks and rain accumulation. The early single-image deraining …

Although deep neural networks enable impressive visual perception performance for
autonomous driving, their robustness to varying weather conditions still requires attention …

Rain fills the atmosphere with water particles, which breaks the common assumption that
light travels unaltered from the scene to the camera. While it is well-known that rain affects …

Existing deep learning-based image deraining methods have achieved promising
performance for synthetic rainy images, typically rely on the pairs of sharp images and …

Rain removal aims to remove rain streaks from images/videos and reduce the disruptive
effects caused by rain. It not only enhances image/video visibility but also allows many …