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 …

Haze usually causes severe interference to image visibility. Such degradation on images
troubles both human observers and computer vision systems. To seek high-quality images …

Model-based single image dehazing was widely studied due to its extensive applications.
Ambiguity between object radiance and haze and noise amplification in sky regions are two …

Images captured in hazy environments need to be processed to increase their contrast and
colour integrity. Dehazing, sometimes referred to as haze removal is an important pre …

Fog removal from an image is an active research topic in computer vision. However, current
literature is weak in the following two areas which in many ways are hindering progress for …

Haze removal from nighttime images is more difficult compared with daytime image
dehazing due to the uneven illumination, low contrast and severe color distortion. In this …

One of the major shortcomings of existing image dehazing algorithms is in estimating scene
transmittance, which has assumed many items in the existing algorithms. One key …

Image dehazing is still an open research topic that has been undergoing a lot of
development, especially with the renewed interest in machine learning-based methods. A …

Images captured in unfavorable weather usually exhibit poor visibility, which results from
scattering and absorption that the propagated light suffers in the atmosphere. To improve the …

We propose a real-time dehazing algorithm for hazy images and videos based on multiscale
guided filtering. The most time-consuming step in physical model-based algorithms is …