A fast and optimized dehazing algorithm for hazy images and videos is proposed in this
work. Based on the observation that a hazy image exhibits low contrast in general, we …

Recent research have shown the potential of using convolutional neural networks (CNNs) to
accomplish single image dehazing. In this paper, we take one step further to explore the …

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 …

Although convolutional neural networks (CNNs) have been proposed to remove adverse
weather conditions in single images using a single set of pre-trained weights, they fail to …

The state-of-the-art deep neural networks are vulnerable to common corruptions (eg, input
data degradations, distortions, and disturbances caused by weather changes, system error …

The presence of hazy particles affects visibility and disturbs the color balance in the
captured videos. Thus, video dehazing is a challenging pre-processing step for high-level …

Hazy weather conditions feed serious haze effects, such as color and visibility degradations
in the captured videos. Extensive research on video dehazing has been carried out …

Haze is a major problem in videos captured in outdoors. Unlike single-image dehazing,
video-based approaches can take advantage of the abundant information that exists across …

Images captured in presence of fog, haze or snow usually suffer from poor contrast and
visibility. In this paper we propose a novel dehazing method to increase visibility from a …

Hazy environmental conditions degrade the quality of captured videos which leads to poor
visibility and color distortion in videos. Such deterioration of captured video quality is mainly …