Single Image Haze Removal Algorithm using Dual DCP and Adaptive Brightness Correction

Dual DCP 및 적응적 밝기 보정을 통한 단일 영상 기반 안개 제거 알고리즘

  • Kim, Jongho (Department of Multimedia Engineering, Sunchon National University)
  • 김종호 (순천대학교 멀티미디어공학과)
  • Received : 2018.08.16
  • Accepted : 2018.11.02
  • Published : 2018.11.30


This paper proposes an effective single-image haze-removal algorithm with low complexity by using a dual dark channel prior (DCP) and an adaptive brightness correction technique. The dark channel of a small patch preserves the edge information of the image, but is sensitive to noise and local brightness variations. On the other hand, the dark channel of a large patch is advantageous in estimation of the exact haze value, but halo effects from block effects deteriorate haze-removal performance. In order to solve this problem, the proposed algorithm builds a dual DCP as a combination of dark channels from patches with different sizes, and this meets low-memory and low-complexity requirements, while the conventional method uses a matting technique, which requires a large amount of memory and heavy computations. Moreover, an adaptive brightness correction technique that is applied to the recovered image preserves the objects in the image more clearly. Experimental results for various hazy images demonstrate that the proposed algorithm removes haze effectively, while requiring much fewer computations and less memory than conventional methods.

SHGSCZ_2018_v19n11_31_f0001.png 이미지

Fig. 1. An example illustration of hazy image acquisition with the optical model

SHGSCZ_2018_v19n11_31_f0002.png 이미지

Fig. 2. Dark channels for different patch sizes (a) Input hazy image (b) 3×3 patch size (c) 31×31 patch size

SHGSCZ_2018_v19n11_31_f0003.png 이미지

Fig. 3. Overall flow diagram of the proposed algorithm

SHGSCZ_2018_v19n11_31_f0004.png 이미지

Fig. 4. Comparison results for the test image (a) Input hazy image (b) Tan's method (c) Fattal's method (d) He's method (e) Proposed method

Table 1. Execution time ratio for comparison of computational complexity

SHGSCZ_2018_v19n11_31_t0001.png 이미지


Supported by : 순천대학교


  1. S. Lee, S. Yun, J. Nam, C. Won, and S. Jung, "A Review on Dark Channel Prior based Image Dehazing Algorithms," EURASIP Journal on Image and Video Processing, no. 4, pp. 1-23, Dec. 2016 DIO:
  2. 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, Nov. 2013. DOI:
  3. Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, "Instant Dehazing of Images Using Polarization," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 325-332, Dec. 2001. DOI:
  4. S. Shwartz, E. Namer, and Y. Y. Schechner, "Blind Haze Separation," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1984-1991, Oct. 2006. DOI:
  5. S. G. Narasimhan and S. K. Nayar, "Contrast Restoration of Weather Degraded Images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 6, pp. 713-724, Jun. 2003. DOI:
  6. S. K. Nayar and S. G. Narasimhan, "Vision in Bad Weather," Proc. 7th IEEE Int'l Conf. Computer Vision (ICCV), vol. 2, pp. 820-827, Sep. 1999. DOI:
  7. 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:
  8. S. G. Narasimhan and S. K. Nayar, "Interactive Deweathering of an Image Using Physical Models," Proc. IEEE Workshop Color and Photometric Methods in Computer Vision, in Conjunction with IEEE Int'l Conf. Computer Vision, Oct. 2003.
  9. R. Tan, "Visibility in Bad Weather from a Single Image," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1-8, Jun. 2008. DOI:
  10. R. Fattal, "Single Image Dehazing," ACM Trans. Graphics, vol. 27, no. 3, pp. 1-9, Aug. 2008. DOI:
  11. K. He, J. Sun, and X. Tang, "Single Image Haze Removal Using Dark Channel Prior," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341-2353, Dec. 2011. DOI:
  12. J. P. Tarel and N. Hautiere, "Fast Visibility Restoration from a Single Color or Gray Level Images," Proc. IEEE Int'l Conf. on Computer Vision (ICCV), pp. 2201-2208, 2009. DOI:
  13. A. Levin, D. Lischinski, and Y. Weiss, "A Closed Form Solution to Natural Image Matting," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, pp. 228-242, Feb. 2008. DOI:
  14. T. Celik, "Spatial Entropy-Based Global and Local Image Contrast Enhancement," IEEE Trans. Image Process., vol. 23, no. 12, pp. 5298-5308, Dec. 2014. DOI:
  15. Z. Mi, H. Zhou, Y. Zheng, and M. Wang, "Single Image Dehazing via Multi-scale Gradient Domain Contrast Enhancement," IET Image Process., vol. 10, no. 3, pp. 206-214, Mar. 2016. DOI: