Single-image Dehazing Algorithm Based on Convolutional Neural Networks

Author(s):  
Jinsheng Xiao ◽  
Li Luo ◽  
Enyu liu ◽  
Junfeng Lei ◽  
Reinhard Klette
2019 ◽  
Vol 128 (1) ◽  
pp. 240-259 ◽  
Author(s):  
Wenqi Ren ◽  
Jinshan Pan ◽  
Hua Zhang ◽  
Xiaochun Cao ◽  
Ming-Hsuan Yang

2019 ◽  
Vol 128 ◽  
pp. 70-77 ◽  
Author(s):  
Cameron Hodges ◽  
Mohammed Bennamoun ◽  
Hossein Rahmani

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.


Author(s):  
Geet Sahu ◽  
Ayan Seal ◽  
Ondrej Krejcar ◽  
Anis Yazidi

2021 ◽  
Vol 30 ◽  
pp. 1100-1115
Author(s):  
Pengyue Li ◽  
Jiandong Tian ◽  
Yandong Tang ◽  
Guolin Wang ◽  
Chengdong Wu

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