Results 151 to 160 of about 11,754,470 (203)
UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder-Decoder. [PDF]
J ImagingZhao A, Li L, Liu S.
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An Image Dehazing Algorithm for Underground Coal Mines Based on gUNet. [PDF]
Sensors (Basel)Tian F, Gao L, Zhang J.
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DST-DETR: Image Dehazing RT-DETR for Safety Helmet Detection in Foggy Weather. [PDF]
Sensors (Basel)Liu Z, Sun C, Wang X.
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Remote sensing image dehazing using generative adversarial network with texture and color space enhancement. [PDF]
Sci RepShen H, Zhong T, Jia Y, Wu C.
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Benchmarking Single-Image Dehazing and Beyond [PDF]
IEEE Transactions on Image Processing, 2019 IEEE Transactions on Image Processing(TIP 2019)
Wenqi Ren, Dacheng Tao, Zhangyang Wang
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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
openaire +1 more source
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.
Wenqi Ren, Nong Sang, Lerenhan Li
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A confidence prior for image dehazing
Pattern Recognition, 2021Abstract By sorting channel-minimized values in an ascending order, we individually put the values of several existing image dehazing priors on the curve of sorted values to propose a framework for unifying and understanding these priors. Then we propose a confidence ratio to specify the probability of each channel-minimized value within a range, and
Feiniu Yuan +4 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
openaire +2 more sources