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 …

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 …

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 …

Image deraining is a typical low-level image restoration task, which aims at decomposing
the rainy image into two distinguishable layers: the clean image layer and the rain layer …

Few existing image defogging or dehazing methods consider dense and non-uniform
particle distributions, which usually happen in smoke, dust and fog. Dealing with these …

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 …

Deep convolutional neural networks (CNNs) have shown their advantages in the single
image de-raining task. However, most existing CNNs-based methods utilize only local …

In dense foggy scenes, existing optical flow methods are erroneous. This is due to the
degradation caused by dense fog particles that break the optical flow basic assumptions …

Many successful optical flow estimation methods have been proposed, but they become
invalid when tested in dark scenes because low-light scenarios are not considered when …

Optical flow has achieved great success under clean scenes, but suffers from restricted
performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing …