Image contrast enhancement using unsharp masking and histogram equalization

  • Shubhi Kansal
  • Shikha Purwar
  • Rajiv Kumar Tripathi
Article
  • 72 Downloads

Abstract

Contrast enhancement and Mean brightness conservation are two important parameters of image enhancement. A high contrast image is good in subjective quality assessment but also high contrast may cause over or under enhancement in the enhanced image. In this paper a new unsharp mask filtering technique with the combination of histogram equalization is used for the general-purpose images which maximizes the entropy of the image as well as controls the over and under enhancement by clipping the histogram of the image. After rigorous experimentation on standard data-set, it is observed that the information present in the image is highest in the proposed method i.e. the entropy value is highest and the mean brightness is also comparable with the other histogram based image enhancement methods. Mean opinion score(MOS) result shows that visual quality of the image is also better than existing methods.

Keywords

Unsharp masking Sharpening Clipping 

Notes

Acknowledgements

This work was supported by Dept. of Electronics and Communication Engineering, NIT Delhi

References

  1. 1.
    Chen S D, Ramli A R (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319CrossRefGoogle Scholar
  2. 2.
    Gonzalez R C, Woods R E (2008) Digital image processing, 3rd edn. Prentice Hall, Englewood CliffsGoogle Scholar
  3. 3.
    Huang S-C, Cheng F-C, Chiu Y-S (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 22 (4):1032–1041MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Kapoor R, Singh K (2014) Image enhancement via median-mean based sub-image clipped histogram equalization. Optik 125:4646–4651CrossRefGoogle Scholar
  5. 5.
    Kaur M, Kaur J, Kaur J (2011) Survey of contrast enhancement techniques based on histogram equalization. Int J Adv Comput Sci Appl (IJACSA) 2(7):137–141MATHGoogle Scholar
  6. 6.
    Kim Y T (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8CrossRefGoogle Scholar
  7. 7.
    Kim M, Chung M G (2008) Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans Consum Electron 54(3):1389–1397CrossRefGoogle Scholar
  8. 8.
    Kotera H, Wang H (2005) Multiscale image sharpening adaptive to edge profile. J Electron Imaging 14(1)Google Scholar
  9. 9.
    Moon T K, Stirling W C (2000) Mathematical methods and algorithms for signal processing. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  10. 10.
    Nithyananda C R, Ramachandra A C, Preethi (2016) Survey on histogram equalization method based image enhancement techniques. In: IEEE international conference on data mining and advanced computing (SAPIENCE)Google Scholar
  11. 11.
    Parihar A S, Verma O P (2016) Contrast enhancement using entropy-based dynamic sub-histogram equalization. IET Image Process 10(11):799–808CrossRefGoogle Scholar
  12. 12.
    Ramponi G (1998) A rational unsharp masking technique. J Electron Imaging 7 (2):333–338CrossRefMATHGoogle Scholar
  13. 13.
    Ritika, Kaur S (2013) Contrast enhancement techniques for images. Int J Comput Appl 64(17):20–25Google Scholar
  14. 14.
    Sim K S, Tso C P, Tan Y Y (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28:1209–1221CrossRefGoogle Scholar
  15. 15.
    Singh K, Kapoor R (2014) Image enhancement using exposure based sub image histogram equalization. Pattern Recogn Lett 36:10–14CrossRefGoogle Scholar
  16. 16.
    Singh K, Kapoor R, Sinha S K (2015) Enhancement of low exposure images via recursive histogram equalization algorithms. Optik 126:2619–2625CrossRefGoogle Scholar
  17. 17.
    Spring K, Russ J C, Mathew J, Hill P, Fellers T, Davidson MW (2016) Unsharp mask filtering. In: Interactive tutorials-optical microscopy primerGoogle Scholar
  18. 18.
    Sridhar S (2011) Digital image processing. Oxford University press, OxfordGoogle Scholar
  19. 19.
    Stark J A (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9(5):889–896CrossRefGoogle Scholar
  20. 20.
    Wang Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45 (1):68–75CrossRefGoogle Scholar
  21. 21.
    Zimmerman JB, Pizer SM, Staab EV, Perry JR, McCartney W, Brenton BC (1988) An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Trans Med Imaging 7(4):304–312CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Institute of TechnologyDelhiIndia

Personalised recommendations