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 …

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …

In this paper, we study two challenging and less-touched problems in single image
dehazing, namely, how to make deep learning achieve image dehazing without training on …

We propose a physics-based feature dehazing network for image dehazing. In contrast to
most existing end-to-end trainable network-based dehazing methods, we explicitly consider …

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 …

Haze, a common kind of bad weather caused by atmospheric scattering, decreases the
visibility of scenes and degenerates the performance of computer vision algorithms. Single …

Image Dehazing is a fast growing research area with several practical applications.
Dehazing improves the image quality that has been affected due to the scattering …

Single image dehazing, which aims at restoring a haze-free image from its correspondingly
unconstrained hazy scene, is a fundamental yet challenging task and has gained immense …

Single-image haze removal is challenging due to its ill-posed nature. The breadth of real-
world scenarios makes it difficult to find an optimal dehazing approach that works well for …

In this paper, we propose a retinex-based decomposition model for a hazy image and a
novel end-to-end image dehazing network. In the model, the illumination of the hazy image …