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 …

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 …

Due to the rapid development of artificial intelligence technology, industrial sectors are
revolutionizing in automation, reliability, and robustness, thereby significantly increasing …

Adverse weather conditions such as fog, haze, snow, mist and glare create visibility
problems for applications of autonomous vehicles. To ensure safe and smooth operations in …

Few existing image defogging or dehazing methods consider dense and non-uniform
particle distributions, which usually happen in smoke, dust and fog. Dealing with these …

The commonly used atmospheric model in image dehazing cannot hold in real cases.
Although deep end-to-end networks were presented to solve this problem by disregarding …

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 …

The quality of images captured in bad weather is often affected by chromatic casts and low
visibility due to the presence of atmospheric particles. Restoration of the color balance is …

Haze reduces the contrast of an image and causes the loss in colors, which has a negative
effect on the subsequent object detection; therefore, single image dehazing is a challenging …

An image can be decomposed into two parts: the basic content and details, which usually
correspond to the low-frequency and high-frequency information of the image. For a hazy …