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 …

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 …

Existing rain-removal algorithms often tackle either rain streak removal or raindrop removal,
and thus may fail to handle real-world rainy scenes. Besides, the lack of real-world deraining …

Single image deraining is important for many high-level computer vision tasks since the rain
streaks can severely degrade the visibility of images, thereby affecting the recognition and …

This paper looks at this intriguing question: are single images with their details lost during
deraining, reversible to their artifact-free status? We propose an end-to-end detail-recovery …

Deep learning based methods have shown dramatic improvements in image rain removal
by using large-scale paired data of synthetic datasets. However, due to the various …

Enhancing the quality of low-light (LOL) images plays a very important role in many image
processing and multimedia applications. In recent years, a variety of deep learning …

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

A deraining network can be interpreted as a conditional generator that aims at removing rain
streaks from image. Most existing image deraining methods ignore model errors caused by …

For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based
methods is mainly affected by the designed deraining models and training datasets. Most of …