haze removal
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2022 ◽  
Vol 9 ◽  
Author(s):  
Yingjie Shi ◽  
Enlai Guo ◽  
Lianfa Bai ◽  
Jing Han

Atmospheric scattering caused by suspended particles in the air severely degrades the scene radiance. This paper proposes a method to remove haze by using a neural network that combines scene polarization information. The neural network is self-supervised and online globally optimization can be achieved by using the atmospheric transmission model and gradient descent. Therefore, the proposed method does not require any haze-free image as the constraint for neural network training. The proposed approach is far superior to supervised algorithms in the performance of dehazing and is highly robust to the scene. It is proved that this method can significantly improve the contrast of the original image, and the detailed information of the scene can be effectively enhanced.


Author(s):  
Hira Khan ◽  
Bin Xiao ◽  
Weisheng Li ◽  
Nazeer Muhammad

2021 ◽  
Author(s):  
Shiqi Huang ◽  
Jie Xu ◽  
Zhigang Liu ◽  
Ke Sun ◽  
Ying Lu

2021 ◽  
Author(s):  
Rajat Tiwari ◽  
Bhawna Goyal ◽  
Ayush Dogra
Keyword(s):  

Author(s):  
Wenbo Zhang ◽  
Weidong Liu ◽  
Le Li ◽  
Jiyu Li ◽  
Meijie Zhang ◽  
...  

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