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 …

Presence of haze in images obscures underlying information, which is undesirable in
applications requiring accurate environment information. To recover such an image, a …

Monocular depth estimation has experienced significant progress on terrestrial images in
recent years thanks to deep learning advancements. But it remains inadequate for …

Most existing single image dehazing methods aim to learn supervised models from paired
synthetic data, which often limits their generalization ability in real-world applications …

Accurate lane detection is an essential function of dynamic traffic perception. Though deep
learning (DL) based methods have been widely applied to lane detection tasks, such …

Dust degrades image content and causes image color cast, which negatively impacts on
many high-level computer vision tasks. In this paper, we proposed a dedusting network with …

Learning-based image dehazing methods are essential to assist autonomous systems in
enhancing reliability. Due to the domain gap between synthetic and real domains, the …

Image dehazing is an ill-posed problem which is far from being solved. Traditional image
dehazing methods often yield mediocre effects and possess substandard processing speed …

In this study, we propose a new approach for training a physics-based dehazing network,
using RGB images and depth maps gathered from a 3D urban virtual environment, with …

In laparoscopic surgery, the smoke generated by operations including electrocautery and
laser ablation seriously degrades the quality of endoscopic images. It not only reduces the …