Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Image Dehazing using Transmission Map Based on Hidden Markov Random Field Model

은닉 마코프 랜덤 모델 기반의 전달 맵을 이용한 안개 제거

  • Lee, Min-Hyuk (Dept. of Control & Instrumentation, Changwon National University) ;
  • Kwon, Oh-Seol (Dept. of Control & Instrumentation, Changwon National University)
  • 이민혁 (창원대학교 제어계측공학과) ;
  • 권오설 (창원대학교 제어계측공학과)
  • Received : 2013.09.30
  • Published : 2014.01.25
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an image haze removal algorithm for a single image. The conventional Dark Channel Prior(DCP) algorithm estimates a transmission map using the dark information in an image, and the haze regions are then detected using a matting algorithm. However, since the DCP algorithm uses block-based processing, block artifacts are invariably formed in the transmission map. To solve this problem, the proposed algorithm generates a modified transmission map using a Hidden Markov Random Field(HMRF) and Expectation-Maximization(EM) algorithm. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal.

본 논문에서는 한 장의 영상에서 안개를 제거하는 알고리즘을 제안한다. 기존의 Dark Channel Prior(DCP) 알고리즘은 영상의 어두운 정보를 계산하여 전달량을 추정한 후, 매팅(matting) 기법을 사용하여 안개 영역을 보완하여 검출한다. 이 과정에서 블록현상이 발생하는 문제가 있으며 이로 인해 안개를 효율적으로 제거하는데 한계점이 있다. 이 문제를 해결하기 위해 본 논문에서는 Hidden Markov Random Field(HMRF) 와 Expectation-Maximization(EM) 알고리즘을 이용하여 매팅 과정에서 발생하는 블록문제를 해결하고자 하였다. 실험 결과를 통하여 제안한 방법은 기존 방법보다 안개제거에서 더 향상된 결과를 얻을 수 있음을 확인하였다.

Keywords

References

  1. Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, "Instant dehazing of images using polarization," Proc. CVPR, pp. 325-332, Hawaii, USA, Dec. 2001.
  2. S. G. Narasimhan and S. K. Nayar, "Chromatic framework for vision in bad weather," Proc. CVPR, pp. 598-605, SC, USA, June 2000.
  3. S. G. Narasimhan and S. K. Nayar, "Contrast restoration of weather degraded image," IEEE trans. Pattern Anal. Mach. Intell., vol. 25, no. 6, pp. 713-724, June 2003. https://doi.org/10.1109/TPAMI.2003.1201821
  4. S. K. Nayar and S. G. Narasimhan, "Vision in bad weather," Proc. ICCV, pp. 820-827, Kerkyra, Greece, Sep. 1999.
  5. J. Korf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, "Deep photo: Model-based photograph enhancement and viewing," Proc. SIGGRAPH Asia, Singapore, Dec. 2008.
  6. R. Tan, "Visibility in bad weather from a single image," Proc. CVPR, pp. 1-8, Anchorage, USA, June 2008.
  7. R. Fattal, "Single image dehazing," ACM Trans. Graphics, vol. 27, no. 3, Aug. 2008.
  8. K. He, J. Sun, and X.Tang, "Single image haze removal using dark channel prior," Proc. CVPR, June 2009.
  9. 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. https://doi.org/10.1109/TPAMI.2007.1177
  10. Q. Wang. "Hmrf-em-image : Implementation of the hidden markov random field and its expectation-maximization algorithm," arXiv: 1207.3510 [cs.CV], 2012.
  11. J. Canny. "A computational approach to edge detection," IEEE Trans. Pattern Anal. Mach., Intell., PAMI vol. 8, no. 6, pp. 679-698, Nov. 1986.
  12. S. Sarkar and K. Boyer, "Optimal infinite impulse response zero crossing based edge detectors," CVGIP : Image Understanding, vol. 54, no. 2, pp. 224-243, Sep. 1991. https://doi.org/10.1016/1049-9660(91)90065-W
  13. A. Saxena, S. Chung, and A. Ng. "3-d depth reconstruction from a single still image," International Journal of Computer Vision, vol. 76, no. 1, pp. 53-69, Jan. 2008.
  14. Raimondo Schettini, Francesca Gasparini, Silvia Corchs and Fabrizio Marini, Contrast image correction method, Journal of Electronic Imaging vol. 19, no. 2, pp. 0230051-02300511, Apr. 2010.