Images captured under foggy or hazy weather conditions are affected by the scattering of
atmospheric particles, resulting in decreased contrast and color variation, thereby limiting …

Haze contamination severely degrades the quality and accuracy of optical remote sensing
(RS) images, including hyperspectral images (HSIs). Currently, there are no paired …

Fast and accurate object detection in foggy weather is crucial for visual tasks such as
autonomous driving and video surveillance. Existing methods typically preprocess images …

The design of a ship detection model that can be adapted to both foggy and clear images
faces significant challenges. Existing methods are either not accurate enough, or have a …

The complexity of urban road scenes at night and the inadequacy of visible light imaging in
such conditions pose significant challenges. To address the issues of insufficient color …

Reducing the atmospheric haze and enhancing image clarity is crucial for computer vision
applications. The lack of real-life hazy ground truth images necessitates synthetic datasets …

Image Dehazing aims to remove atmospheric fog or haze from an image. Although the
Dehazing models have evolved a lot in recent years, few have precisely tackled the problem …

Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze
from images and restore clean background details. Recognizing the limitations of traditional …

Compared to pixel-level content loss, domain-level style loss in CycleGAN-based dehazing
algorithms just imposes relatively soft constraints on the intermediate translated images …

While numerous solutions leveraging convolutional neural networks and Transformers have
been proposed for image dehazing, there remains significant potential to improve the …