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Enhancement dark channel theory algorithm of fog image based on fourth-order PDE model


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Enhancement dark channel theory algorithm of fog image based on fourth-order PDE model
Gao Yin1Yun Lijun2Shi Junsheng1Li Chengli1
1.Color and Image Vision Lab;
2.College of Information,Yunnan Normal University,Kunming 650500,China
image enhancement fourth-order partial differential equation model tolerance mechanism dark channel theory
TP751; TP391
To deal with the image hue and brightness distortion problems in the classic dark channel theory algorithm,the enhancement dark channel algorithm of fog image based on the fourth-order partial differential equations(PDE)model is proposed here.According to the threshold,the image is segmented by the tolerance mechanism and distinguishes between bright areas and dark channel areas,the transmission image is smoothed by the introduced fourth-order PDE model,and the image is precisly corrected by the tolerance mechanism.An defogging enhancement image is obtained through the dark channel theory.The subjective observation and objective evaluation show that the algorithm is better than the classic dark channel algorithm in the overall and details.


[1] Narasimhan T S G,Nayar S K.Interactive(de)weathering of an image using physical models[A].The 9th IEEE International Conference on Computer Vision[C].Nice,France:IEEE Computer Society,2003:1-8.
You Qian,Li Ying,Li Yucheng,et al.Research on fog-degraded image restoration based on bilateral filter of RGB channel[J].Computer Engineering and Applications,2014,50(6):157-160.
[3]He K,Sun J,Tang X.Guided image filtering[A].The 11th European Conference on Computer Vision[C].Crete,Greece:Springer Berlin Heidelberg,2010:1-14.
Guo Jia,Wang Xiaotong,Hu Chengpeng,et al.Single image dehazing based on scene depth and physical model[J].Journal of Image and Graphics,2012,17(1):27-32.
Xue Mogen,Zhou Pucheng,Zhang Hongkun.Single foggy image restoration using orientation extended fieleds of experts[J].Journal of Computer-aided Design & Computer Graphics,2014,26(5):782-787.
[6]He K,Sun J,Tang X.Single image haze removal using dark channel prior[J].IEEE Tranfactions on Pattem Analysis & Intelligence,2011,32(12):2341-2353.
[7]Xu Haoran,Guo Jianming,Liu Qing,et al.Fast image dehazing using improved dark channel prior[A].Information Science and Technology(ICIST),2012 International Conference on[C].Wuhan,China:IEEE,2012:663-667.
[8]Dubok Park,Hanseok K O.Fog-degraded image restoration using characteristics of RGB channel in single monocular image[A].Consumer Electronics 2012 IEEE International Conference on[C].Las Vegas,USA:IEEE,2012:139-140.
[9]Perona P,Malik J.Scale-space and edge detection using anisotropic diffusion[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):629-639.
[10]Catte F,Lions P L,Morel J M,et al.Image selective smoothing and edge detection by nonlinear diffusion[J].SLAM Journal on Numerical Analysis,1992,29(1):182-193.

[11]You Yuli,Kaveh M.Fourth-order partial differential equations for noise removal[J].IEEE Transaction Image Processing,2000,9(10):1723-1729.

Jiang Jianguo,Hou Tianfeng,Qi Meibin.Improved algorithm on image haze removal using dark channel prior[J].Journal of Circuits and Systerns,2011,16(2):7-12.

Wang Hongnan,Zhong Wen,Wang Jing,et al.Research of measurement for digital image definition[J].Journal of Image and Graphics,2004,9(7):828-831.
Li Qi,Feng Huajun,Xu Zhihai,et al.Digital image sharpness evaluation function[J].Acta Photonica Sinica,2002,31(6):736-738.
[15]Eskicioglu Ahmet M,Fisher Paul S.Image quality measures and their performance[J].IEEE Transactions on Communications,1995,43(12):2959-2965.


Last Update: 2015-10-31