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High-Speed and High-Quality Haze Removal Method Based on Dual Dark Channels
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  • Journal title : Journal of Broadcast Engineering
  • Volume 20, Issue 5,  2015, pp.697-705
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2015.20.5.1
 Title & Authors
High-Speed and High-Quality Haze Removal Method Based on Dual Dark Channels
Moon, Sun-A; Kim, Won-Tae; Kim, Tae-Hwan;
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 Abstract
This paper proposes a high-speed and high-quality haze removal method based on dual dark channels. In the conventional method, the halo artifacts are suppressed by the additional transmission refinement, but the transmission refinement is computationally intensive and the quality of the haze removal is sometimes unsatisfactory because of the residual halo artifacts. In the proposed method, the transmission is estimated with the mixture of the two dark channels with different window size. By mixing the two dark channels so as to avoid the halo artifacts, the proposed method realizes a high-quality haze removal even without the transmission refinement. Experimental results demonstrate that the quality of the results by the proposed method is superior to those by the conventional method and the speed of the haze removal is about 14.2 times higher than that of the conventional method.
 Keywords
haze removal;halo artifact;dual dark channels;halo region prediction;transmission estimation;
 Language
Korean
 Cited by
 References
1.
K. He, J. Sun, and X. Tang, Single image haze removal using dark channel prior, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, Dec. 2011. crossref(new window)

2.
K. He, J. Sun, and X. Tang, Guided image filtering, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, Jun. 2013. crossref(new window)

3.
S. C. Pei, and T. Y. Lee, Effective image haze removal using dark channel prior and post-processing, IEEE International Symposium on Circuit and Systems, pp. 2777-2780, May. 2012.

4.
R. He, Z. Wang, Y. Fan and D. D. Feng, Multiple scattering model based single image dehazing, IEEE Conference on Industrial Electronics and Applications, pp. 733-737, Jun. 2013.

5.
S. C. Pei, and T. Y. Lee, Nighttime haze removal using color transfer pre-processing and dark channel prior, IEEE International Conference on Image Processing, pp. 957-960, Oct. 2012.

6.
R. Gao, X. Fan, J. Zhang and Z. Luo, Haze filtering with aerial perspective, IEEE International Conference on Image Processing, pp. 989-992, Sept. 2012.

7.
Gibson, Kristofor B., Dung T. Vo, and Truong Q. Nguyen, An investigation of dehazing effects on image and video coding, IEEE Transactions on Image processing, vol. 21, no. 2, pp. 662-673, Sept. 2012. crossref(new window)

8.
Y. H. Shiau, H. Y. Hung, P. Y. Chen and Y. Z. Chuang, Hardware implementation of a fast and efficient haze removal method, IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no.8, pp. 1369-1374, Aug. 2013. crossref(new window)

9.
C. O. Ancuti, C. Ancuti, C. Hermans, and P. Bekaert, A Fast Semi-Inverse Approach to Detect and Remove the Haze from a Single Image, Asian Conference on Computer Vision, pp. 501-514, 2011.

10.
R. Fattal, Single image dehazing, ACM Transactions on Graphics, vol. 27, no.3, pp. 72, Aug. 2008. crossref(new window)

11.
S. G. Narasimhan and S. K. Nayar, Chromatic framework for vision in bad weather, IEEE Conference on Computer Vision and Pattern Recognition, pp. 598-605, 2000.

12.
S. G. Narasimhan and S. K. Nayar, Vision and the atmosphere, International Journal of Computer Vision, vol. 48, no.3, pp. 233-254, Aug. 2002. crossref(new window)

13.
R. Tan, Visibility in bad weather from a single image, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, Jun. 2008.