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 …

Recent years have witnessed significant advances in image deraining due to the kinds of
effective image priors and deep learning models. As each deraining approach has …

Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

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 …

Images captured in snowy days suffer from noticeable degradation of scene visibility, which
degenerates the performance of current vision-based intelligent systems. Removing snow …

Rain streaks and raindrops are two natural phenomena, which degrade image capture in
different ways. Currently, most existing deep deraining networks take them as two distinct …

Deep learning algorithms have recently achieved promising deraining performances on
both the natural and synthetic rainy datasets. As an essential low-level pre-processing …

Rain is a common natural phenomenon. Taking images in the rain however often results in
degraded quality of images, thus compromises the performance of many computer vision …

Most state-of-the-art object detection methods have achieved impressive perfomrace on
several public benchmarks, which are trained with high definition images. However, existing …

Recently stereo image deraining has attracted lots of attention due to its superiority of
abundant information from cross views. Exploring interaction information across stereo …