Images acquired in poor weather conditions (haze, fog, smog, mist, etc.) are often severely
degraded. In the atmosphere, there exists two types of particles: dry particles (dust, smoke …

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 …

In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …

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 …

Owing to the fast developments of economics, a lot of devices and objects have been
connected and have formed the Internet of Things (IoT). Visual sensors have been applied …

Convolutional neural network (CNN)-based autonomous driving object detection algorithms
have excellent detection results on conventional datasets, but the detector performance can …

The performance of deep learning-based denoisers highly depends on the quantity and
quality of training data. However, paired noisy–clean training images are generally …

This article presents U 2PNet, a novel unsupervised underwater image restoration network
using polarization for improving signal-to-noise ratio and image quality in underwater …

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration.
However, limited by factors such as imaging hardware, scanning time, and cost, it is …

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low
light, intense glow, light scattering, and the presence of multicolored light sources. Existing …