Removing adverse weather conditions like rain, fog, and snow from images is an important
problem in many applications. Most methods proposed in the literature have been designed …

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the
exploration of rain removal from videos or a single image has drawn considerable attention …

Deep learning (DL) methods have achieved state-of-the-art performance in the task of single
image rain removal. Most of current DL architectures, however, are still lack of sufficient …

Removing rain streaks from a single image has been drawing considerable attention as rain
streaks can severely degrade the image quality and affect the performance of existing …

Many methods have set state-of-the-art performance on restoring images degraded by bad
weather such as rain, haze, fog, and snow, however they are designed specifically to handle …

Single image rain streak removal is an extremely challenging problem due to the presence
of non-uniform rain densities in images. We present a novel density-aware multi-stream …

The Vision Transformer (ViT) architecture has been remarkably successful in image
restoration. For a while, Convolutional Neural Networks (CNN) predominated in most …

Most deraining works focus on rain streaks removal but they cannot deal adequately with
heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or …

Rain is a common weather phenomenon, where object visibility varies with depth from the
camera and objects faraway are visually blocked more by fog than by rain streaks. Existing …

Single image rain removal is a typical inverse problem in computer vision. The deep
learning technique has been verified to be effective for this task and achieved state-of-the-art …