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 …

Image dehazing is always a hot topic in the field of computer vision since haze has
significant impact on the imaging quality of camera. Therefore, many image dehazing …

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 …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

We propose an effective image dehazing algorithm which explores useful information from
the input hazy image itself as the guidance for the haze removal. The proposed algorithm …

Image dehazing is a challenging task in computer vision. Currently, most dehazing methods
adopt the U-Net architecture that directly fuses the decoding layer with the corresponding …

Scene recovery is a fundamental imaging task with several practical applications, including
video surveillance and autonomous vehicles, etc. In this article, we provide a new real-time …

State-of-the-art deep dehazing models are often difficult in training. Knowledge distillation
paves a way to train a student network assisted by a teacher network. However, most …

Most of the existing deep learning-based dehazing methods are trained and evaluated on
the image dehazing datasets, where the dehazed images are generated by only exploiting …

Recently, there has been rapid and significant progress on image dehazing. Many deep
learning based methods have shown their superb performance in handling homogeneous …