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 …

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 …

Image dehazing using learning-based methods has achieved state-of-the-art performance in
recent years. However, most existing methods train a dehazing model on synthetic hazy …

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 …

In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy
input. The proposed algorithm hinges on an end-to-end trainable neural network that …

This work addresses the problem of semantic foggy scene understanding (SFSU). Although
extensive research has been performed on image dehazing and on semantic scene …

Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant
attention in the last few years. Despite this growing interest, the scientific community is still …

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 …