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MCRFS-Net: single image dehazing based on multi-scale contrastive regularization and frequency selection. [PDF]
Sci RepQin Q, Shui L, Zhang Y, Song S, Jiang J.
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Self-Parameter Distillation Dehazing
IEEE Transactions on Image Processing, 2023In this paper, we propose a novel dehazing method based on self-distillation. In contrast to conventional knowledge distillation approaches that transfer large models (teacher networks) to small models (student networks), we introduce a single knowledge distillation network that transfers network parameters to itself for dehazing.
Guisik Kim, Junseok Kwon
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Knowledge Transfer Dehazing Network for NonHomogeneous Dehazing
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Single image dehazing is an ill-posed problem that has recently drawn important attention. It is a challenging image process task, especially in nonhomogeneous scene. However, the existing dehazing methods are commonly designed to handle homogeneous haze which is easily violated in practice, due to the unknown haze distribution of real world.
Haiyan Wu +4 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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Optimizing polarization dehazing
Modern Physics Letters B, 2021In the process of haze removal by the polarization method, it is very important to obtain accurate polarization information. On the one hand, the influence of noise can be effectively suppressed by appropriately weighted amplifying the airlight degree of polarization and the airlight corresponding to an object at an infinite distance.
Miao Wu, Chunmin Zhang
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Real time polarimetric dehazing
Applied Optics, 2013Remote sensing is a rich topic due to its utility in gathering detailed accurate information from locations that are not economically feasible traveling destinations or are physically inaccessible. However, poor visibility over long path lengths is problematic for a variety of reasons.
Jason, Mudge, Miguel, Virgen
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Adaptive dehazing control factor based fast single image dehazing
Multimedia Tools and Applications, 2019The single image dehazing is performed using atmospheric scattering model (ASM). The ASM is based on transmission and atmospheric light. Thus, accurate estimation of transmission is essential for quality single image dehazing. Single image dehazing is of prime focus in research nowadays.
Suresh Chandra Raikwar +1 more
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2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020
Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy- free
Jing Liu +4 more
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Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy- free
Jing Liu +4 more
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