Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

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
Download PDF
⟨ Previous Next ⟩

Abstract

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.

본 논문에서는 효과적이고 저 복잡도를 갖는 단일 영상 기반의 안개 제거를 위하여 dual dark channel prior (DCP)와 적응적인 밝기 보정 기법을 이용하는 알고리즘을 제안한다. 작은 크기의 패치에 의한 dark channel은 영상의 에지 정보를 잘 보존하지만 국부적인 잡음 및 밝기 변화에 민감한 반면, 큰 크기의 패치에 의한 dark channel은 정확한 안개 값을 추정하는데 유리하지만 블록 현상과 이로 인한 후광 효과는 안개 제거 성능을 떨어뜨린다. 이러한 문제를 해결하기 위하여 기존의 방법에서는 계산량 및 메모리 요구량이 큰 matting 기법을 활용한 반면, 제안하는 방법은 크기가 다른 패치로부터 구한 dark channel을 합성하여 dual DCP를 구성하고, 이를 이용하여 안개를 제거함으로써 적은 계산량 및 메모리 요구량을 달성한다. 또한 안개 성분을 제거한 영상에 적응적 밝기 보정 기법을 적용하여 영상에 포함된 객체가 선명하게 보존될 수 있도록 한다. 안개 성분이 포함된 다양한 영상에 대해 수행한 실험 결과 제안하는 안개 제거 기법이 기존의 방법에 비해 안개 제거 성능이 우수하면서 계산량과 메모리 요구량이 감소함을 알 수 있다.

Keywords

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

References

  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: https://dx.doi.org/10.1186/s13640-016-0104-y
  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: https://dx.doi.org/10.1364/OE.21.027127
  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: https://dx.doi.org/10.1109/CVPR.2001.990493
  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: https://dx.doi.org/10.1109/CVPR.2006.71
  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: https://dx.doi.org/10.1109/TPAMI.2003.1201821
  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: https://dx.doi.org/10.1109/ICCV.1999.790306
  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: https://dx.doi.org/10.1145/1409060.1409069
  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: https://dx.doi.org/10.1109/CVPR.2008.4587643
  10. R. Fattal, "Single Image Dehazing," ACM Trans. Graphics, vol. 27, no. 3, pp. 1-9, Aug. 2008. DOI: https://dx.doi.org/10.1145/1360612.1360671
  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: https://dx.doi.org/10.1109/TPAMI.2010.168
  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: https://dx.doi.org/10.1109/ICCV.2009.5459251
  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: https://dx.doi.org/10.1109/TPAMI.2007.1177
  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: https://dx.doi.org/10.1109/TIP.2014.2364537
  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: https://dx.doi.org/10.1049/iet-ipr.2015.0112