Abstract Automated Driving Systems (ADS) open up a new domain for the automotive
industry and offer new possibilities for future transportation with higher efficiency and …

Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image
pairs, many recent methods attempted to improve models' generalization ability by training …

Considering the ill-posed nature, contrastive regularization has been developed for single
image dehazing, introducing the information from negative images as a lower bound …

Existing dehazing approaches struggle to process real-world hazy images owing to the lack
of paired real data and robust priors. In this work, we present a new paradigm for real image …

Under the nighttime haze environment, the quality of acquired images will be deteriorated
significantly owing to the influences of multiple adverse degradation factors. In this paper …

Nighttime image dehazing is a challenging task due to the presence of multiple types of
adverse degrading effects including glow, haze, blur, noise, color distortion, and so on …

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 …

Despite single image dehazing has been made promising progress with Convolutional
Neural Networks (CNNs), the inherent equivariance and locality of convolution still …

Recent years have witnessed significant progress in the area of single image dehazing,
thanks to the employment of deep neural networks and diverse datasets. Most of the existing …