DOI QR코드

DOI QR Code

A Novel Method of Determining Parameters for Contrast Limited Adaptive Histogram Equalization

대비제한 적응 히스토그램 평활화에서 매개변수 결정방법

  • Min, Byong-Seok (Department of Digital Electronic Communications, Chungcheong University) ;
  • Cho, Tae-Kyung (Department of Information & Telecommunications Engineering, Sangmyung University)
  • 민병석 (충청대학교 디지털전자통신과) ;
  • 조태경 (상명대학교 정보통신공학과)
  • Received : 2012.10.19
  • Accepted : 2013.03.07
  • Published : 2013.03.31

Abstract

Histogram equalization, which stretches the dynamic range of intensity, is the most common method for enhancing the contrast of image. Contrast limited adaptive histogram equalization(CLAHE), proposed by K. Zuierveld, has two key parameters: block size and clip limit. These parameters mainly control image quality, but have been heuristically determined by user. In this paper, we propose a novel method of determining two parameters of CLAHE using entropy of image. The key idea is based on the characteristics of entropy curves: clip limit vs entropy and block size vs entropy. Clip limit and block size are determined at the point with maximum curvature on entropy curve. Experimental results show that the proposed method improves images with very low contrast.

히스토그램 평활화는 영상의 밝기 분포를 변화시킴으로써 화질을 향상시키는 방법으로 다양한 분야에서 응용되고 있다. 전역적인 방법은 영상 밝기의 전체적인 분포를 균등 분포로 변환함으로써 영상의 밝기가 과도하게 변하는 단점을 갖고 있다. 이를 해결하기 위한 방법으로 K. Zuierveld가 제안한 대비 제한 적응 히스토그램 평활화(CLAHE)가 실용적으로 널리 사용되고 있다. 이 방법에서는 블록단위의 처리를 위한 블록 크기와 대비 제한을 위한 매개변수 등 두 개의 매개변수가 히스토그램의 평활화 성능을 결정하는데, 이것들을 결정하는 구체적인 알고리듬은 없으며 실험적으로 시행착오학습 통해 결정한다. 본 논문에서는 영상의 엔트로피에 기반해서 CLAHE의 매개변수인 블록 크기와 대비제한 매개변수를 결정하는 새로운 방법을 제안한다. 제안한 방법은 CLAHE를 자동화할 수 있으며, 전체적으로 어두운 영상이나 밝은 영상에 적용한 결과 전역적인 방법에 비해 주관적 화질 개선의 효과를 나타내었다.

Keywords

References

  1. R. Gonzalez, R. Wood, Digital Image Processing, 3rd ed., Pearson Education, 2009.
  2. R. Gonzalez, R. Woods, S. Eddins, Digital Image Processing Using MATLAB, 2nd ed., Prentice Hall, 2003.
  3. W. Burger, M. Burge, Principles of Digital Image Processing, Springer-Verlag, 2009. DOI: http://dx.doi.org/10.1007/978-1-84800-191-6
  4. A. Bovik, The Essential Guide to Image Processing, Academic Press, 2009.
  5. R. Grag, B. Mittal, S. Grag, "Histogram Equalization Techniques For Image Enhancement," International Journal of Electronics & Communication Technology, pp. 107-111, 2011.
  6. M. Kaur, J. Kaur, J. Kaur, "Survey of Contrast Enhancement Techniques based on Histogram Equalization," International Journal of Advanced Computer Science and Application, Vol. 2, No.7, pp.137-141, 2011.
  7. R. Sharmila, R. Uma, "A New Approach To Image Contrast Enhancement using Weighted Threshold Histogram Equalization with Improved Switching Median Filter," International Journal of Advanced Engineering Sciences and Technologies, Vol. 7, No. 2, pp, 208-211, 2011.
  8. Y_T Kim, "Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization." IEEE Trans. on Consumer Electronics, Vol. 43, No. 1, pp. 1-8. 1997. DOI: http://dx.doi.org/10.1109/30.580378
  9. C. Wang, Z. Ye, "Brightness Preserving Histogram Equalization with Maximum Entropy: A Variational Perspective," IEEE Trans. on Consumer Electronics, Vol. 51, No. 4, pp.1326-1334, 2005. DOI: http://dx.doi.org/10.1109/TCE.2005.1561863
  10. H. Ibrahim, N. Kong, "Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement," IEEE Trans. on Consumer Electronics, Vol. 53, No. 4, pp. 1752-1758, 2007. DOI: http://dx.doi.org/10.1109/TCE.2007.4429280
  11. J. Yoo, S. Ohm, M. Chung, "Maximum-Entropy Image Enhancement Using Brightness Mean and Variance," Journal of Korean Socieity Internet Information, Vol. 13, No. 3, 2012. DOI: http://dx.doi.org/10.7472/jksii.2012.13.3.61
  12. Y. Wang, Q. Chen, B. M. Zhang, "Image Enhancement based on Equal Area Dualistic sub-Image Histogram Equalization Method," IEEE Trans. on Consumer Electronics, Vol. 45, No. 1, pp.68-75, 1999. DOI: http://dx.doi.org/10.1109/30.754419
  13. H. Yoon, Y. Han, H. Hahn, "Contrast Enhancement Using a Density based Sub-histogram Equalization Technique," Journal of The Institute of Electronics Engineers of Korea, Vol.46, SC, No.1, pp.61-72, 2009.
  14. K. Zuiderveld, "Contrast Limited Adaptive Histogram Equalization," Academic Press Inc., 1994.
  15. A. Reza, "Realization of the Contrast Limited Adaptive Histogram Equalization(CLAHE) for Real-Time Image Enhancement," Journal of VLSI Signal Processing, Vol. 38, pp. 35-44, 2004. DOI: http://dx.doi.org/10.1023/B:VLSI.0000028532.53893.82
  16. S. Chen, A. Ramli, "Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scable Brightness Preservation," IEEE Trans. on Consumer Electronics, Vol. 49, No. 4, pp. 1301-1309, 2003. DOI: http://dx.doi.org/10.1109/TCE.2003.1261233
  17. P. Peebles, Probability, Random Variables, and Random Signal Principles, 3rd ed., McGraw-Hill, 1993.
  18. R. Gray, Entropy and Information Theory, Springer-Verlag, 1990. DOI: http://dx.doi.org/10.1007/978-1-4757-3982-4
  19. J. Arora, Introduction to Optimum Design, 2nd, Academic Press, 2004.
  20. Mathworks Inc., Matlab 7 Function Reference, 2004.
  21. T. Finney, Calculus and Analytic Geometry, 8th ed., Addison-Wesley, 1992.

Cited by

  1. Face Recognition using High-order Local Pattern Descriptor and DCT-based Illuminant Compensation vol.21, pp.1, 2016, https://doi.org/10.5909/JBE.2016.21.1.51