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 …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Rainy weather is a challenge for many vision-oriented tasks (eg, object detection and
segmentation), which causes performance degradation. Image deraining is an effective …

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 challenging task since rain streaks have the characteristics of a
spatially long structure and have a complex diversity. Existing deep learning-based methods …

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 …

This survey article is concerned with the emergence of vision augmentation AI tools for
enhancing the situational awareness of first responders (FRs) in rescue operations. More …

Single image deraining is typically addressed as residual learning to predict the rain layer
from an input rainy image. For this purpose, an encoder-decoder network draws wide …

The images captured by under-display cameras (UDCs) are degraded by the screen in front
of them. We model this degradation in terms of a) diffraction by the pixel grid, which …