Image Contrast Enhancement by Illumination Change Detection

조명 변화 감지에 의한 영상 콘트라스트 개선

  • Received : 2013.09.01
  • Accepted : 2014.04.08
  • Published : 2014.04.25


There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.


  1. B.Xie, V.Ramesh, and T.Boult,"Sudden Illumination Change Detection Using Order Consistency", Image and Vision Computing, vol. 22, no. 2, pp. 117-125, February 2004.
  2. Yoo-Joo Choi, Je-Sung Lee and We-Duke Cho, "A Robust Hand Recognition In Varying Illumination", Advances in Human-Computer Interaction, Chapter 4, pp 53-70. InTech Education and Publishing, Publication date: October 2008.
  3. Bayanmunkh.O and Chang-Hoon Lee, "Sudden Illumination Change Detection Using Local Region Information and Fuzzy Logic", Proceedings of KIIS Spring Conference 2013 Vol. 23, No. 1.
  4. Krasimira Kapitanova, Sang H. Son and Kyoung-Don Kang "Using fuzzy logic for robust event detection in wireless sensor network"
  5. Jyh-Shing and Roger Jang, "Adaptive Network-Based Fuzzy Inference System", IEEE Transactions on Systems, Man, and Cybernetics, Vol.23, No.3, May/June 1993.
  6. Srikanta Patnaik abd Yeon-Mo Yang, "Soft Computing Techniques in Vision Science".
  7. Ritika Assistant Prof and Sandeep Kaur Assistant Prof, "Contrast Enhancement Techniques for Images A Visual Analysis", International Journal of Computer Applications (0975 - 8887) Volume 64- No.17, February 2013
  8. Zujun Hou and Wei-Yun Yau, "Visible Entropy: A Measure for Image Visibility", 2010 International Conference on Pattern Recognition.
  9. Evaluation metrics,