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Domain Adaptation for Image Dehazing [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize well to real hazy images due to domain shift.
Yuanjie Shao +4 more
openaire +3 more sources
An Improved Generative Adversarial Network for Image Dehazing [PDF]
Jisuanji gongcheng, 2022 Hazy weather degrades the quality of images obtained by computer vision related systems and affects the normal operation of the system.However, most traditional algorithms establish the relationship between the clear image model and image distortion, and
DING Yongjun, HUANG Shan
doaj +1 more source
DFR-Net: Density Feature Refinement Network for Image Dehazing Utilizing Haze Density Difference [PDF]
IEEE transactions on multimedia, 2023 In the image dehazing task, the haze density is a key feature that affects the performance of dehazing methods. The haze density difference, which has rarely been utilized in previous methods, can guide networks to perceive different global densities and
Zhong-Ze Wang +4 more
semanticscholar +1 more source
Efficient Dehazing Method for Outdoor and Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 As an atmospheric phenomenon, haze significantly reduces the visibility of outdoor and remote sensing images. As remote sensing and outdoor imaging have different mechanisms, existing dehazing methods are hard to be applied for both outdoor images and ...
Chenyang Li +6 more
doaj +1 more source
Real-time image dehazing by superpixels segmentation and guidance filter [PDF]
Journal of Real-Time Image Processing, 2020 Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed.
Haseeb Hassan +6 more
semanticscholar +2 more sources
Semi‐supervised learning dehazing algorithm based on the OSV model
IET Image Processing, 2023 Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging.
Lijun Zhu +5 more
doaj +1 more source
A Model-Driven Deep Dehazing Approach by Learning Deep Priors
IEEE Access, 2021 Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks.
Dong Yang, Jian Sun
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A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning
Journal of Marine Science and Engineering, 2023 Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information.
Zhenzhen Zhou +3 more
doaj +1 more source
Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction
IEEE Access, 2020 In order to alleviate adverse impacts of haze on high-level vision tasks, image dehazing attracts great attention from computer vision research field in recent years.
Qiaosi Yi +4 more
doaj +1 more source