In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

Night images suffer not only from low light, but also from uneven distributions of light. Most
existing night visibility enhancement methods focus mainly on enhancing low-light regions …

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 …

Image restoration aims to reconstruct a sharp image from its degraded counterpart, which
plays an important role in many fields. Recently, Transformer models have achieved …

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 …

Convolutional neural networks (CNNs) have achieved significant success in the single
image dehazing task. Unfortunately, most existing deep dehazing models have high …

We present a comprehensive study and evaluation of existing single-image dehazing
algorithms, using a new large-scale benchmark consisting of both synthetic and real-world …

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 …

Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a
certain model from a source domain to a target domain. UDA is of particular significance …

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 …