To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image
pairs, many recent methods attempted to improve models' generalization ability by training …

Image restoration under adverse weather conditions has been of significant interest for
various computer vision applications. Recent successful methods rely on the current …

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 …

Vision-based intelligent driving assistance systems and transportation systems can be
improved by enhancing the visibility of the scenes captured in extremely challenging …

Hazy images captured under ill-posed scenarios with scattering medium (ie haze, fog, or
smoke) are contaminated in visibility. Inevitably, these images are further degraded by …

Saturation information in hazy images is conducive to effective haze removal, However,
existing saturation-based dehazing methods just focus on the saturation value of each pixel …

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 …

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low
light, intense glow, light scattering, and the presence of multicolored light sources. Existing …

Due to the complexity of real environment and potential defects of current simulation
datasets, either prior-based or deep learning-based single image dehazing methods may …

Model-based single image dehazing algorithms restore haze-free images with sharp edges
and rich details for real-world hazy images at the expense of low PSNR and SSIM values for …