In this paper, we propose a level-aware progressive network (LAP-Net) for single image
dehazing. Unlike previous multi-stage algorithms that generally learn in a coarse-to-fine …

Single image dehazing is the ill-posed two-dimensional signal reconstruction problem.
Recently, deep convolutional neural networks (CNN) have been successfully used in many …

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 …

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 …

This paper reviews the second challenge on image dehazing (restoration of rich details in
hazy image) with focus on proposed solutions and results. The training data consists from 55 …

In this work, a single image dehazing method that improves the haze removal capacity of the
Joint Contrast Enhancement and Exposure Fusion (CEEF) method with Smoothing …

The presence of haze significantly degrades the quality of remote sensing images, resulting
in issues such as color distortion, reduced contrast, loss of texture, and blurred image edges …

The automatic differentiation of retinal vessels into arteries and veins (A/V) is a highly
relevant task within the field of retinal image analysis. However, due to limitations of retinal …

In this paper, we present a new approach for single image dehazing based on the proposed
variational optimization. A hazy image captures the information about haze in terms of the …

Image dehazing is a method to reduce the effects of haze, dust, or fog in images in order to
provide a clear view of the observed scene. A large variety of traditional and machine …