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A Single Image Defogging Algorithm Based on Multi-Resolution Method Using Histogram Information and Dark Channel Prior

히스토그램 정보와 dark channel prior를 이용한 다해상도 기반 단일 영상 안개 제거 알고리즘

  • Yang, Seung-Yong (Department of Control and Instrumental Engineering, Graduate School of Korea Maritime and Ocean University) ;
  • Yang, Jeong-Eun (Department of Control and Instrumental Engineering, Graduate School of Korea Maritime and Ocean University) ;
  • Hong, Seok-Keun (Division of Information Technology Engineering, Korea Maritime and Ocean University) ;
  • Cho, Seok-Je (Division of Information Technology Engineering, Korea Maritime and Ocean University)
  • Received : 2015.06.10
  • Accepted : 2015.07.17
  • Published : 2015.07.31

Abstract

In this paper, we propose a defogging algorithm for a single image. Dark channel prior (DCP), which is a well-known defogging algorithm, can cause halo artifacts on boundary regions, low-contrast defogging images, and requires a large computational time. To solve these problems, we use histogram information with DCP on transmission estimation regions and a multi-resolution method. Local histogram information can reduce the low-contrast problem on a defogging image, and the multi-resolution method with edge information can reduce the total computational time and halo artifacts. We validate the proposed method by performing experiments on fog images, and we confirm that the proposed algorithm is more efficient and superior than conventional algorithms.

본 논문에서는 효과적인 단일 영상 안개 제거 알고리즘을 제안한다. 잘 알려진 안개 제거 알고리즘인 dark channel prior(DCP)는 경계선 영역에서의 후광 현상(halo artifact) 및 결과 영상의 저대비를 초래하고 전달량 정제(refinement) 과정에서 긴 계산 시간을 필요로 한다. 이러한 문제들을 해결하기 위해 제안한 방법은 전달량을 추정할 때 DCP와 히스토그램 정보로 구성된 비용함수를 이용하고, 빠른 처리를 위해 다해상도 기법을 이용한다. 히스토그램 정보는 안개 제거 결과의 저대비 현상을 방지해주고, 에지 정보를 참고하는 다해상도 기법은 계산 시간을 감소시키고 후광 현상을 방지할 수 있다. 다수의 안개 영상에 대한 실험을 통해 제안한 방법이 기존의 방법들보다 효율적이고 우수함을 확인하였다.

Keywords

References

  1. E. R. Davies, Machine Vision, Third Edition : Theory, Algorithms, Practicallities (Signal Processing and its Applications), Morgan Kaufmann, 2005.
  2. R. T. Tan, "Visibility in bad weather from a single image," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
  3. J. H. Kim, J. Y. Sim, and C. S. Kim, "Single image dehazing based on contrast enhancement," Proceedings of the IEEE Acoustics, Speech and Signal Processing, pp. 1273-1276, 2011.
  4. K. He, J. Sun, and X. Tang, "Single image haze removal using dark channel prior," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, 2011. https://doi.org/10.1109/TPAMI.2010.168
  5. Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, "Instant dehazing of images using polarization," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1984-1991, 2001.
  6. S. Shwartz, E. Namer, and Y. Y. Schechner, "Blind haze separation," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1984-1991, 2006.
  7. S. G. Narasimhan and S. K. Nayar, "Chromatic framework for vision in bad weather," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 598-605, 2000.
  8. S. G. Narasimhan and S. K. Nayar, "Contrast restoration of weather degraded image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 713-724, 2003. https://doi.org/10.1109/TPAMI.2003.1201821
  9. J. Kopf, B. Neubert, B. Chen, M. Cohen, C. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, "Deep photo: Model-based photograph enhancement and viewing," ACM Transactions on Graphics, vol. 27, no. 5, pp. 1-10, 2008.
  10. K. Gibson, D. Vo, and T. Nguyen, "An inversigation of dehazing effects on image and video coding," IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 662-673, 2012. https://doi.org/10.1109/TIP.2011.2166968
  11. R. Fattal, "Single image dehazing," ACM Transactions on Graphics, vol. 27, no. 3, pp. 1-9, 2008.
  12. K. He, J. Sun, and X. Tang, "Guided image filtering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409. 2012. https://doi.org/10.1109/TPAMI.2012.213
  13. S. Yang, Q. Zhu, J. Wang, D. Wu, and Y. Xie, "An improved single image haze removal algorithm based on dark channel prior and histogram specification," Proceedings of the 3rd International Conference on Multimedia Technology, pp. 279-292, 2013.
  14. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Third Edition, Prentice Hall, 2007.
  15. N. Hautiere, J. Tarel, D. Aubert, and E. Dumout, "Blind contrast enhancement assessment by gradient ratioing at visible edges," Image Analysis & Stereology Journal, vol. 27, no. 2, pp. 1-7, 2008. https://doi.org/10.5566/ias.v27.p1-10