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Contrastive Learning-Driven Image Dehazing with Multi-Scale Feature Fusion and Hybrid Attention Mechanism. [PDF]
J ImagingZhang H, Wang J, Tu X, Niu Z, Wang Y.
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Underwater Turbid Media Stokes-Based Polarimetric Recovery. [PDF]
Sensors (Basel)Wang Z, Hu M, Zhang K.
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RSP-YOLOv11n multi-module optimized algorithm for insulator defect detection in UAV images. [PDF]
Sci RepZheng B +3 more
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Privacy-preserving federated prediction of health outcomes using multi-center survey data. [PDF]
BMC Med Res MethodolDas S +11 more
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Semi-Supervised Image Dehazing
IEEE Transactions on Image Processing, 2020We present an effective semi-supervised learning algorithm for single image dehazing. The proposed algorithm applies a deep Convolutional Neural Network (CNN) containing a supervised learning branch and an unsupervised learning branch. In the supervised branch, the deep neural network is constrained by the supervised loss functions, which are mean ...
Lerenhan Li +6 more
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IEEE Transactions on Image Processing, 2020
In this paper, we study two less-touched challenging problems in single image dehazing neural networks, namely, how to remove haze from a given image in an unsupervised and zeroshot manner. To the ends, we propose a novel method based on the idea of layer disentanglement by viewing a hazy image as the entanglement of several "simpler" layers, i.e., a ...
Boyun Li +5 more
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In this paper, we study two less-touched challenging problems in single image dehazing neural networks, namely, how to remove haze from a given image in an unsupervised and zeroshot manner. To the ends, we propose a novel method based on the idea of layer disentanglement by viewing a hazy image as the entanglement of several "simpler" layers, i.e., a ...
Boyun Li +5 more
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The Visual Computer, 2021
Image dehazing aims to remove the haze noise and restore the image content from hazy images. It is a challenging task because of the unbalanced distribution of the haze noise and the variety of the image contents. Most existing methods apply convolutional neural networks to learn the dehazing process by blind end-to-end training, which relies on the ...
Fei Yang, Qian Zhang
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Image dehazing aims to remove the haze noise and restore the image content from hazy images. It is a challenging task because of the unbalanced distribution of the haze noise and the variety of the image contents. Most existing methods apply convolutional neural networks to learn the dehazing process by blind end-to-end training, which relies on the ...
Fei Yang, Qian Zhang
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Visual-quality-driven unsupervised image dehazing
Neural Networks, 2023Most of the existing learning-based dehazing methods require a diverse and large collection of paired hazy/clean images, which is intractable to obtain. Therefore, existing dehazing methods resort to training on synthetic images. This may result in a possible domain shift when treating real scenes.
Aiping Yang +6 more
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Task-Oriented Network for Image Dehazing
IEEE Transactions on Image Processing, 2020Haze interferes the transmission of scene radiation and significantly degrades color and details of outdoor images. Existing deep neural networks-based image dehazing algorithms usually use some common networks. The network design does not model the image formation of haze process well, which accordingly leads to dehazed images containing artifacts and
Runde Li +4 more
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