Exposure and Median Based One-to-One Gray Level Mapping Transformation for Entropy Preservation and Contrast Enhancement

  • M. Eswar ReddyEmail author
  • Gudheti Ramachandra Reddy
Research Article


Many histogram equalization (HE) techniques have been proposed for the contrast enhancement in the past. In recent years clipped histogram equalization techniques are developed to control the degree of over enhancement and the noise. Yet these methods are not guaranteed to preserve the gray levels and thus the information in output image is less than that in the input image, even though it has been enhanced. We propose two new one-to-one gray level mapping (OGM) transformation methods, namely exposure based one-to-one gray level mapping (EOGM) transformation and median based one-to-one gray level mapping (MOGM) transformation. In EOGM and MOGM methods histogram is divided into two sub histograms based on exposure and median of the images respectively. Weights for these sub histograms are calculated and then OGM transformation function is applied to these sub histograms by using the derived weights. This transformation addresses both over enhancement and gray level loss effectively and also ensure uniform degree of enhancement. This preserves all the information content even after enhancement with all structural details, ensures no false contouring. Thus they are suitable for medical image applications, where information loss leads to wrong diagnosis. The experimental results show the supremacy of our methods over existing HE methods.


Gray level loss Over enhancement Gray level mapping Weights Image information content 

Supplementary material

40010_2018_497_MOESM1_ESM.docx (16.4 mb)
Supplementary material 1 (DOCX 16829 kb)


  1. 1.
    Gonzalez RC, Woods RE (2012) Digital image processingGoogle Scholar
  2. 2.
    Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8CrossRefGoogle Scholar
  3. 3.
    Chen SD, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Chen SD, Ramli AR (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309CrossRefGoogle Scholar
  6. 6.
    Sim KS, Tso CP, Tan YY (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28(10):1209–1221CrossRefGoogle Scholar
  7. 7.
    Abdullah-Al-Wadud M, Kabir MH, Dewan MA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600CrossRefGoogle Scholar
  8. 8.
    Ibrahim H, Kong NS (2007) Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(4):1752–1758CrossRefGoogle Scholar
  9. 9.
    Ooi CH, Isa NA (2010) Quadrants dynamic histogram equalization for contrast enhancement. IEEE Trans Consum Electron 56(4):2552–2559CrossRefGoogle Scholar
  10. 10.
    Kim M, Chung MG (2008) Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans Consum Electron 54(3):1389–1397CrossRefGoogle Scholar
  11. 11.
    Wang Q, Ward RK (2007) Fast image/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans Consum Electron 53(2):757–764CrossRefGoogle Scholar
  12. 12.
    Ooi CH, Kong NS, Ibrahim H (2009) Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans Consum Electron 55(4):2072–2080CrossRefGoogle Scholar
  13. 13.
    Singh K, Kapoor R (2014) Image enhancement using exposure based sub image histogram equalization. Pattern Recogn Lett 36:10–14CrossRefGoogle Scholar
  14. 14.
    Singh K, Kapoor R (2014) Image enhancement via median-mean based sub-image-clipped histogram equalization. Optik Int J Light Electron Opt 125(17):4646–4651CrossRefGoogle Scholar
  15. 15.
    Hanmandlu M, Verma OP, Kumar NK, Kulkarni M (2009) A novel optimal fuzzy system for color image enhancement using bacterial foraging. IEEE Trans Instrum Meas 58(8):2867–2879CrossRefGoogle Scholar
  16. 16.
    Xue Wufeng et al (2014) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process 23(2):684–695ADSMathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© The National Academy of Sciences, India 2018

Authors and Affiliations

  1. 1.VIT UniversityVelloreIndia

Personalised recommendations