A software supported image enhancement approach based on DCT and quantile dependent enhancement with a total control on enhancement level

  • Mayank TiwariEmail author
  • Subir Singh Lamba
  • Bhupendra Gupta


In many computer vision applications like medical imaging, pattern recognition etc., image enhancement is an important pre-processing requirement which is used to improve the efficiency of an application. A significantly large literature is available on image enhancement; unfortunately, most of these schemes have certain shortcomings for e.g. the lack of control over the contrast starching, noise enhancement and ‘mean-shift’ problem etc. To deal with the aforementioned problems, this study suggests an efficient method which is based on discrete cosine transformation (DCT) and quantile dependent sub-division of the histogram of given input image. In the proposed method, we apply DCT on the input image to get low-frequency component (LFC) and then use the quantile-based sub-division on the histogram of LFC. Finally, histogram equalization is performed on all these sub-histograms separately. The main advantage of quantile-based segmentation is that here entire intensity spectrum participates in the enhancement process, which provides a total control over the enhancement level. In the proposed method the high-frequency component remains untouched and hence the structural information of the input image and the noise in the input image remains unaffected by the image enhancement process.


Discrete cosine transform Contrast enhancement Linearly quantile separated histogram equalization Greyscale images Mean-shift problem 



Authors would like to thanks to all the reviewers for their useful suggestion and precious time for reviewing this work. Their suggestion help us a lot to improve the quality of the paper. Also we are grateful to the editor associated with this paper for his/her cooperation and support.


  1. 1.
    Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transform. IEEE Trans Comput 100(1):90–93MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen S, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319CrossRefGoogle Scholar
  3. 3.
    Chen S, Ramli AR (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309CrossRefGoogle Scholar
  4. 4.
    Gonzalez RC, Woods RE (2008) Digital Image Processing, 3rd edn. M.A. Addison-Wesley, ReadingGoogle Scholar
  5. 5.
    Gupta B, Agarwal TK (2017) Linearly quantile separated weighted dynamic histogram equalization for contrast enhancement. Computers & Electrical Engineering, online available at
  6. 6.
    Gupta B, Tiwari M (2018) Improving performance of source-camera identification by suppressing peaks and eliminating low-frequency defects of reference SPN. IEEE Signal Process Lett 25(9):1340–1343CrossRefGoogle Scholar
  7. 7.
    Gupta B, Tiwari M (2018) Improving source camera identification performance using DCT based image frequency components dependent sensor pattern noise extraction method. Digit Investig 24:121–127CrossRefGoogle Scholar
  8. 8.
    Kim M, Chung GC (2008) Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancements. IEEE Trans Consum Electron 54(3):1389–1397CrossRefGoogle Scholar
  9. 9.
    Leng L, Ming L, Zhang JS (2010) Histogram equalization algorithm with local adaptive enhancement based on edge details. Microelectronics and Computer 27:38–41Google Scholar
  10. 10.
    Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. International Journal of the Physical Sciences 5(17):2543–2554Google Scholar
  11. 11.
    Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: 2010 international conference on information and communication technology convergence (ICTC), Jeju, pp 467–471.
  12. 12.
    Lidong H, Wei Z, Jun W, Zebin S (2015) Combination of contrast limited adaptive histogram equalisation and discrete wavelet transformation for image enhancement. IET Image Process 9(10):908–915CrossRefGoogle Scholar
  13. 13.
    Ling Z, Liang Y, Wang Y, Shen H, Lu X (2015) Adaptive extended piecewise histogram equalisation for dark image enhancement. IET Image Process 9 (11):1012–1019CrossRefGoogle Scholar
  14. 14.
    Pizer SM (1987) Adaptive histogram equalization and its variations. Computer Vision, Graphics and Image Processing 39:355–368CrossRefGoogle Scholar
  15. 15.
    Sim KS, Tso CP, Tan Y (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28:1209–1221CrossRefGoogle Scholar
  16. 16.
    The USC-SIPI Image Database, Accessed online from
  17. 17.
    Tiwari M, Gupta B (2018) Image features dependant correlation-weighting function for efficient PRNU based source camera identification. Forensic Sci Int 285:111–120CrossRefGoogle Scholar
  18. 18.
    Tiwari M, Gupta B, Shrivastava M (2014) High speed quantile based histogram equalization for brightness preservation and contrast enhancement. IET Image Process 9(1):80–89CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Yeong TK (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8CrossRefGoogle Scholar
  21. 21.
    Zuiderveld K (1994). In: Heckbert P.S. (ed) Contrast Limited Adaptive Histogram Equalization. Chapter VIII, Graphics Gems IV. Academic Press, Cambridge, pp 474–485Google Scholar

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

Authors and Affiliations

  1. 1.Department of MathematicsPDPM Indian Institute of Information Technology, Design & ManufacturingJabalpurIndia

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