High-quality clear images are the basis for advanced vision tasks such as target detection
and semantic segmentation. This paper proposes an image dehazing algorithm named …

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

Real-world nonhomogeneous haze brings challenges to image restoration. More efforts are
needed to remove dense haze and thin haze simultaneously and efficiently. However, most …

Image dehazing is a fundamental low-level vision task and has gained increasing attention
in the computer community. Most existing learning-based methods achieve haze removal by …

Haze severely affects computer vision algorithms by degrading the quality of captured
images and results in image data loss. With several available approaches for dehazing …

Aiming to solve the problem of color distortion and loss of detail information in most
dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature …

Learning-based methods have attracted considerable interest in image dehazing. However,
most existing methods are not well adapted to different hazy conditions, especially when …

It is very challenging to perform dehazing operations on single haze images taken due to
information degradation, for which we propose a novel single-image dehazing network …

Image dehazing has been a popular topic of research for a long time. Previous deep
learning-based image dehazing methods have failed to achieve satisfactory dehazing …

In the past decade, significant research effort has been directed toward developing single-
image dehazing algorithms. Despite this effort, dehazing continues to present a challenge …