Multispectral remote sensing images are often contaminated by haze, which causes low
image quality. In this paper, a novel dehazing method based on a deep convolutional neural …

The haze in remote sensing images can cause the decline of image quality and bring many
obstacles to the applications of remote sensing images. Considering the non-uniform …

The haze weather environment leads to the deterioration of the visual effect of the image,
and it is difficult to carry out the work of the advanced vision task. Therefore, dehazing the …

Different from the ground image with uniform haze, the haze in remote sensing (RS) image
has the characteristics of irregular shape and uneven concentration in hazy weather. It …

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-
uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network …

Multispectral remote sensing (RS) images are often contaminated by the haze that degrades
the quality of RS data and reduces the accuracy of interpretation and classification …

In this paper, we propose a deep convolutional network for single image dehazing based on
derived image fusion strategy. Instead of estimating the transmission map and atmospheric …

To address the problems that most convolutional neural network-based image defogging
algorithm models capture incomplete global feature information and incomplete defogging …

Dehazing based on deep learning neural networks (CNNs) has achieved remarkable
results. However, the most existing dehazing CNNs perform well only on synthetic images …

There is a series of image degradation in the image acquired in haze and other weather.
The single image dehazing is a challenging and ill-posed problem. Using deep neural …