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 …

Image dehazing is a representative low-level vision task that estimates latent haze-free
images from hazy images. In recent years, convolutional neural network-based methods …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

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 …

In this paper, we propose a novel image dehazing framework with frequency and spatial
dual guidance. In contrast to most existing deep learning-based image dehazing methods …

With the development of convolutional neural networks, hundreds of deep learning–based
dehazing methods have been proposed. In this article, we provide a comprehensive survey …

We show that raw degradation features can effectively guide deep restoration models,
providing accurate degradation priors to facilitate better restoration. While networks that do …

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 …

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free
images from observed hazy images. Some existing deep learning based methods are …

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