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Image Enhancement Based on Quotient Space

  • Tong Zhao
  • Guoyin Wang
  • Bin Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)

Abstract

Histogram equalization (HE) is a simple and widely used method in the field of image enhancement. Recently, various improved HE methods have been developed to improve the enhancement performance, such as BBHE, DSIHE and PC-CE. However, these methods fail to preserve the brightness of original image. To address the insufficient of these methods, an image enhancement method based on quotient space (IEQS) is proposed in this paper. Quotient space is an effective approach that can partitions the original problem in different granularity spaces. In this method, different quotient spaces are combined and the final granularity space is generated using granularity synthesis algorithm. The gray levels in each interval are mapped to the appropriate output gray-level interval. Experimental results show that IEQS can enhance the contrast of original image while preserving the brightness.

Keywords

histogram equalization quotient space image enhancement granularity synthesis 

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References

  1. 1.
    Kong, N.S.P., Ibrahim, H.: Color image enhancement using brightness preserving dynamic histogram equalization. IEEE Transactions on Consumer Electronics 54(4), 1962–1968 (2008)CrossRefGoogle Scholar
  2. 2.
    Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics 43(1), 1–8 (1997)CrossRefGoogle Scholar
  3. 3.
    Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics 45(1), 68–75 (1999)CrossRefGoogle Scholar
  4. 4.
    Lee, C., Lee, C., Lee, Y.Y., et al.: Power-constrained contrast enhancement for emissive displays based on histogram equalization. IEEE Transactions on Image Processing 21(1), 80–93 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Zhang, L., Zhang, B.: The quotient space theory of problem solving. Fundamenta Informaticae 59(2), 287–298 (2004)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Wang, G., Zhang, Q.: Granular Computing based cognitive computing. In: 8th IEEE International Conference on Cognitive Informatics, ICCI 2009, pp. 155–161. IEEE, Hong Kong (2009)CrossRefGoogle Scholar
  7. 7.
    Yao, J., Vasilakos, A.V., Pedrycz, W.: Granular computing: Perspectives and challenges, pp. 1–13 (2013)CrossRefGoogle Scholar
  8. 8.
    Liang, Y., Mao, Z.: A Method of Segmenting Texture of Targets in Remote Sensing Images Based on Granular Computing. In: 2011 International Conference on Information Technology, Computer Engineering and Management Sciences (ICM), pp. 280–283. IEEE, Nanjing (2011)CrossRefGoogle Scholar
  9. 9.
    Zou, B., Jia, Q., Zhang, L., et al.: Target detection based on granularity computing of quotient space theory using SAR image. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4601–4604. IEEE, Hong Kong (2010)CrossRefGoogle Scholar
  10. 10.
    Chen, X., Wu, Y., Cheng, H.: Quotient space granular computing for the Click-stream data warehouse in Web servers. In: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE), pp. 93–96. IEEE, Chengdu (2010)Google Scholar
  11. 11.
    Celik, T., Tjahjadi, T.: Automatic image equalization and contrast enhancement using Gaussian mixture modeling. IEEE Transactions on Image Processing 21(1), 145–156 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tong Zhao
    • 1
  • Guoyin Wang
    • 1
    • 2
  • Bin Xiao
    • 1
  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent TechnologyCASChongqingChina

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