DOI QR코드

DOI QR Code

A Novel Image Dehazing Algorithm Based on Dual-tree Complex Wavelet Transform

  • Huang, Changxin (School of Aeronautics and Astronautics, Sichuan University) ;
  • Li, Wei (School of Aeronautics and Astronautics, Sichuan University) ;
  • Han, Songchen (School of Aeronautics and Astronautics, Sichuan University) ;
  • Liang, Binbin (School of Aeronautics and Astronautics, Sichuan University) ;
  • Cheng, Peng (School of Aeronautics and Astronautics, Sichuan University)
  • Received : 2017.12.10
  • Accepted : 2018.04.15
  • Published : 2018.10.31

Abstract

The quality of natural outdoor images captured by visible camera sensors is usually degraded by the haze present in the atmosphere. In this paper, a fast image dehazing method based on visible image and near-infrared fusion is proposed. In the proposed method, a visible and a near-infrared (NIR) image of the same scene is fused based on the dual-tree complex wavelet transform (DT-CWT) to generate a dehazed color image. The color of the fusion image is regulated through haze concentration estimated by dark channel prior (DCP). The experiment results demonstrate that the proposed method outperforms the conventional dehazing methods and effectively solves the color distortion problem in the dehazing process.

Keywords

References

  1. J. H. Kim, J. Y. Sim and C. S. Kim, "Single image dehazing based on contrast enhancement," in Proc. of IEEE Conf. on Acoustics, Speech and Signal Processing, pp.1273-1276, May, 2011.
  2. Y. Zhou, Q. W. Li and G. Y. Huo, "Adaptive image enhancement using nonsubsampled contourlet transform domain histogram matching," Chinese Optics Letters. vol.12, no. s2, pp.S21002-321005, 2014.
  3. R. Fattal, "Single image dehazing," ACM Transactions on Graphics, vol. 27, no. 3, pp.1-9, 2008.
  4. K. M. He, J. Sun, X. O. Tang. "Single image haze removal using dark channel prior," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp.1956-1963, June, 2009.
  5. K. B. Gibson, D. T. Vo and T. Q. Nguyen. "An investigation of dehazing effects on image and video coding," IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 662-73, 2012. https://doi.org/10.1109/TIP.2011.2166968
  6. A. Levin, D. Lischinski and Y. Weiss. "A closed form solution to natural image matting," IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 30, no. 2, pp. 228-242, 2008. https://doi.org/10.1109/TPAMI.2007.1177
  7. H. B. Liu, J. Yang, Z. P. Wu et al. "Fast single image dehazing based on image fusion," Journal of Electronic Imaging, vol. 24, no. 1, pp. 013020(1-10), 2015.
  8. Y. Y. Gao, H. M. Hu and S. H. Wang. "A fast image dehazing algorithm based on negative correction," Signal Processing, vol. 103, no. 10, pp. 380-398, 2014. https://doi.org/10.1016/j.sigpro.2014.02.016
  9. D. Berman, T. Treibitz and S. Avidan. "Non-local image dehazing," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1674-1682, 2016.
  10. L. Schaul, C. Fredembach and S. Susstrunk. "Color image dehazingg using the near-infrared," in Proc. of IEEE Conf. on Image Processing, pp. 1629-1632, 2009.
  11. Z. Farbman, R. Fattal, D. Lischinski. "Edge-preserving decompositions for multi-scale tone and detail manipulation," ACM Transactions on Graphics, vol. 27, no. 3, pp.1-10, 2008.
  12. N.G. Kingsbury. "The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters," in Proc. of IEEE Conf. on Image Processing, pp. 319 - 322, 1998.
  13. N. G. Kingsbury. "A dual-tree complex wavelet transform with improved orthogonality and symmetry properties," in Proc. of IEEE Conf. on Image Processing, vol. 2, no. 4, 375-378, 2000.
  14. K. M. He, J. Sun and X. O. Tang. "Guided image filtering," in Proc. of IEEE Conf. on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, 2013.
  15. AT Young. "Rayleigh scattering," Applied Optics, vol. 20, no. 4, pp.533, 1981. https://doi.org/10.1364/AO.20.000533
  16. C. Fredembach and S. Susstrun. "Colouring the near infrared," in Proc. of IS&T Conf. on Color Imaging, pp. 176-182, 2008.
  17. W. Wang and F. Chang. "A Multi-focus image fusion method based on laplacian pyramid," Journal of Computers, vol. 6, no.12, pp. 2559-2566, 2011.
  18. A. Toet. "Image fusion by a ratio of low-pass pyramid," Pattern Recognition Letters, vol. 9, no. 4, pp.245-253, 1989. https://doi.org/10.1016/0167-8655(89)90003-2
  19. F. Nencini, A. Garzelli, S. Baronti and L. Alparone. "Remote sensing image fusion using the curvelet transform," Information Fusion, vol. 8, no.2, pp.143-156, 2007. https://doi.org/10.1016/j.inffus.2006.02.001
  20. T. Shibata. M. Tanaka and M. Okutomi. "Versatile visible and near-infrared image fusion based on high visibility area selection," Journal of Electronic Imaging, vol. 25, no. 1, vol. 25, no. 1, pp.013016, 2016. https://doi.org/10.1117/1.JEI.25.1.013016
  21. H. Wang, Z. L. Jing and J. X. Li. "Multi-focus image fusion using image black segment," Journal of Shanghai Jiaotong University, vol. 37, no. 11, pp. 1743 -1750, 2003. https://doi.org/10.3321/j.issn:1006-2467.2003.11.025
  22. Z. Wang, A. C. Bovik and H. R. Sheikh, "Image quality assessment: from error visibility to structural similarity," IEEE Transaction on Image Process. Vol. 13, no. 4, pp. 600-612, 2004. https://doi.org/10.1109/TIP.2003.819861
  23. N. Hautiere, J. P Tarel, D. Aubert et al, "Blind contrast enhancement assessment by gradient ratioing at visible edges," Image Analysis and Stereology Journal, Vol. 27, no. 2, pp. 87-95,2011. https://doi.org/10.5566/ias.v27.p87-95