Volume 11 Number 1
February 2014
Article Contents
Fan Guo, Jin Tang and Zi-Xing Cai. Image Dehazing Based on Haziness Analysis. International Journal of Automation and Computing, vol. 11, no. 1, pp. 78-86, 2014. doi: 10.1007/s11633-014-0768-7
Cite as: Fan Guo, Jin Tang and Zi-Xing Cai. Image Dehazing Based on Haziness Analysis. International Journal of Automation and Computing, vol. 11, no. 1, pp. 78-86, 2014. doi: 10.1007/s11633-014-0768-7

Image Dehazing Based on Haziness Analysis

Author Biography:
  • Fan Guo received the B.Sc. degree from the Central South University, China in 2005, M.Sc. and Ph.D. degrees in computer science from Central South University, China in 2008 and 2012, respectively. Currently, she is a postdoctoral fellow at the School of Information Science and Engineering, Central South University. Her research interests include image processing, pattern recognition and virtual reality. E-mail: guofancsu@163.com

  • Corresponding author: Jin Tang
  • Received: 2012-09-15
Fund Project:

This work was supported by National Natural Science Foundation of China (Nos.91220301, 61175064 and 61273314), Postdoctoral Science Foundation of Central South University (No.126648), New Teacher Fund for School of Information Science and Engineering, Central South University (No.2012170301)

通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Image Dehazing Based on Haziness Analysis

  • Corresponding author: Jin Tang
Fund Project:

This work was supported by National Natural Science Foundation of China (Nos.91220301, 61175064 and 61273314), Postdoctoral Science Foundation of Central South University (No.126648), New Teacher Fund for School of Information Science and Engineering, Central South University (No.2012170301)

Abstract: We present two haze removal algorithms for single image based on haziness analysis. One algorithm regards haze as the veil layer, and the other takes haze as the transmission. The former uses the illumination component image obtained by retinex algorithm and the depth information of the original image to remove the veil layer. The latter employs guided filter to obtain the refined haze transmission and separates it from the original image. The main advantages of the proposed methods are that no user interaction is needed and the computing speed is relatively fast. A comparative study and quantitative evaluation with some main existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods. On the top of haze removal, several applications of the haze transmission including image refocusing, haze simulation, relighting and 2-dimensional (2D) to 3-dimensional (3D) stereoscopic conversion are also implemented.

Fan Guo, Jin Tang and Zi-Xing Cai. Image Dehazing Based on Haziness Analysis. International Journal of Automation and Computing, vol. 11, no. 1, pp. 78-86, 2014. doi: 10.1007/s11633-014-0768-7
Citation: Fan Guo, Jin Tang and Zi-Xing Cai. Image Dehazing Based on Haziness Analysis. International Journal of Automation and Computing, vol. 11, no. 1, pp. 78-86, 2014. doi: 10.1007/s11633-014-0768-7
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