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Edge-Preserving and Adaptive Transmission Estimation for Effective Single Image Haze Removal

  • Kim, Jongho (Department of Multimedia Engineering, Sunchon National University)
  • Received : 2020.02.17
  • Accepted : 2020.02.27
  • Published : 2020.05.31

Abstract

This paper presents an effective single image haze removal using edge-preserving and adaptive transmission estimation to enhance the visibility of outdoor images vulnerable to weather and environmental conditions with computational complexity reduction. The conventional methods involve the time-consuming refinement process. The proposed transmission estimation however does not require the refinement, since it preserves the edges effectively, which selects one between the pixel-based dark channel and the patch-based dark channel in the vicinity of edges. Moreover, we propose an adaptive transmission estimation to improve the visual quality particularly in bright areas like sky. Experimental results with various hazy images represent that the proposed method is superior to the conventional methods in both subjective visual quality and computational complexity. The proposed method can be adopted to compose a haze removal module for realtime devices such as mobile devices, digital cameras, autonomous vehicles, and so on as well as PCs that have enough processing resources.

Keywords

References

  1. C. Yeh, L. Kang, M. Lee, and C. Lin, "Haze effect removal from image via haze density estimation in optical model," Optics Express, Vol. 21, No. 22, pp. 27127-27141, Nov. 2013. DOI: http://dx.doi.org/10.1364/OE.21.027127
  2. J. Tarel and N. Hautiere, "Fast visibility restoration from a single color or gray level images," in Proc. IEEE Int. Conference on Computer Vision (ICCV), pp. 2201-2208, Sep. 29-Oct. 2, 2009. DOI: http://dx.doi.org/10.1109/ICCV.2009.5459251
  3. Y. Schechner, S. Narasimhan, and S. Nayer, "Instant dehazing of images using polarization," in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 325-332, Dec. 8-14, 2001. DOI: http://dx.doi.org/10.1109/CVPR.2001.990493
  4. S. Narasimhan and S. Nayer, "Contrast restoration of weather degraded images," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, No. 6, pp. 713-724, June 2003. DOI: http://dx.doi.org/10.1109/TPAMI.2003.1201821
  5. J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, "Deep photo: Model-based photograph enhancement and viewing," ACM Trans. Graphics, Vol. 27, No. 5, pp. 116:1-116:10, Dec. 2008. DOI: http://dx.doi.org/10.1145/1409060.1409069
  6. S. Lee, S. Yun, J. Nam, C. Won, and S. Jung, "A review on dark channel prior based image dehazing algorithms," The European Association for Signal Processing (EURASIP) Journal on Image and Video Processing, Vol. 2016, No. 4, pp. 1-23, Dec. 2016. DOI: http://dx.doi.org/10.1186/s13640-016-0104-y
  7. R. Tan, "Visibility in bad weather from a single image," in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, June 23-28, 2008. DOI: http://dx.doi.org/10.1109/CVPR.2008.4587643
  8. R. Fattal, "Single image dehazing," ACM Trans. Graphics, Vol. 27, No. 3, pp. 1-9, Aug. 2008. DOI: http://dx.doi.org/10.1145/1360612.1360671
  9. K. He, J. Sun, and X. Tand, "Single image haze removal using dark channel prior," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, pp. 2341-2353, Dec. 2011. DOI: http://dx.doi.org/10.1109/TPAMI.2010.168
  10. J. Kim, "Histogram modification based on additive term and gamma correction for image contrast enhancement," Journal of the Korea Institute of Electronic Communication Science, Vol. 13, No. 5, pp. 1117-1124, Oct. 2018. https://doi.org/10.13067/JKIECS.2018.13.5.1117
  11. J. Kim, "Single image haze removal algorithm using dual DCP and adaptive brightness correction," Journal of the Korea Academia-Industrial cooperation Society (JKAIS), Vol. 19, No. 11, pp. 31-37, Nov. 2018. DOI: http://dx.doi.org/10.5762/KAIS.2018.19.11.31
  12. W. Oh and J. Kim, "Single image haze removal technique via pixel-based joint BDCP and hierarchical bilateral filter," Journal of the Korea Institute of Electronic Communication Science, Vol. 14, No. 1, pp. 257-264, Feb. 2019. https://doi.org/10.13067/JKIECS.2019.14.1.257
  13. J. Kim, "Efficient single image dehazing by pixel-based JBDCP and low complexity transmission estimation," Journal of the Korea Institute of Electronic Communication Science, Vol. 14, No. 5, pp. 977-984, Oct. 2019.
  14. J. Kim, "Low complexity single image dehazing via edge-preserving transmission estimation and pixel-based JBDC," Journal of the Korea Academia-Industrial cooperation Society (JKAIS), Vol. 20, No. 12, pp. 1-7, Dec. 2019. DOI: http://dx.doi.org/10.5762/KAIS.2019.20.12.1
  15. T. Yu, I. Riaz, J. Piao, and H. Shin, "Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior," IET Image Processing, Vol. 9, No. 9, pp. 725-734, Sep. 2015. DOI: http://dx.doi.org/10.1049/iet-ipr.2015.0087
  16. S. Salazar-Colores, J. Ramos-Arreguin, J. Pedraza-Ortega, and J. Rodriguez-Resendiz, "Efficient single image dehazing by modifying the dark channel prior," The European Association for Signal Processing (EURASIP) Journal on Image and Video Processing, Vol. 2019:66, No. 1, pp. 1-8, May 2019. DOI: http://dx.doi.org/10.1186/s13640-019-0447-2
  17. B. Li, S. Wang, J. Zheng, and L. Zheng, "Single image haze removal using content-adaptive dark channel and post enhancement," IET Computer Vision, Vol. 8, No. 2, pp. 131-140, Apr. 2014. DOI: http://dx.doi.org/10.1049/iet-cvi.2013.0011
  18. A. Levin, D. Lischinski, and Y. Weiss, "A closed form solution to natural image matting," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 30, No. 2, pp. 228-242, Feb. 2008. DOI: http://dx.doi.org/10.1109/TPAMI.2007.1177