Skip to main content
SpringerLink
Log in

Improved Image Enhancement of Natural Images with Median Mean-Based Sub-Image Clipped Histogram Equalization

  • Conference paper
  • First Online:
Soft Computing for Security Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1397))

  • 1103 Accesses

Abstract

This paper introduces a powerful image contrast enhancement algorithm that is developed based on the energy curve equalization technique. Instead of the histogram, an energy curve is used for considering spatial contextual information in the image. This is Improved Image Enhancement of natural images with median mean-based sub-image clipped histogram equalization. The algorithm consists of the following steps, first, obtaining the energy curve, secondly, calculating median and mean intensity values of the image, third, the energy curve is clipped using a threshold level which is its mean occupancy, and fourth, the clipped energy curve is divided into two halves based on median and then further partitioned into four subparts based on mean intensity values, all these four portions are equalized and then combined to form an enhanced image. This promotes natural enhancement and levies control over the rate of enhancement. The simulation conveys that the proposed method shows supremacy over the other previously existing methods. This shows an increase in entropy which is the information content held in the resultant image.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 2nd edn. (Prentice-Hall, Englewood Cliffs, NJ, 2002)

    Google Scholar 

  2. Y.T. Kim, Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43, 1–8 (1997)

    Article  Google Scholar 

  3. Y. Wan, Q. Chen, B.M. Zhang, Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electron. 45, 68–75 (1999)

    Article  Google Scholar 

  4. S.D. Chen, A.R. Ramli, Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49, 1310–1319 (2003)

    Article  Google Scholar 

  5. S.D. Chen, A.R. Ramli, Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49, 1301–1309 (2003)

    Article  Google Scholar 

  6. K.S. Sim, C.P. Tso, Y.Y. Tan, Recursive sub-image histogram equalization applied to grayscale images. Pattern Recogn. Lett. 28, 1209–1221 (2007)

    Article  Google Scholar 

  7. M. Kim, M.G. Chung, Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans. Consum. Electron. 54, 1389–1397 (2008)

    Article  Google Scholar 

  8. Q. Wang, R.K. Ward, Fastimage/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans. Consum. Electron. 53, 757–764 (2007)

    Article  Google Scholar 

  9. T. Kim, J. Paik, Adaptive contrast enhancement using gain-controllable clipped histogram equalization. IEEE Trans. Consum. Electron. 54, 1803–1810 (2008)

    Article  Google Scholar 

  10. C.H. Ooi, N.S.P. Kong, H. Ibrahim, Bi-histogram with a plateau limit for digital image enhancement. IEEE Trans. Consum. Electron. 55, 2072–2080 (2009)

    Google Scholar 

  11. M. Abdullah-Al-Wadud et al., A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53, 593–600 (2007)

    Article  Google Scholar 

  12. H. Ibrahim, N.S.P. Kong, Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53, 1752–1758 (2007)

    Article  Google Scholar 

  13. D. Sheet, H. Garud, A. Suveer, M. Mahadevappa, J. Chatterjee, Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56, 2475–2480 (2010)

    Article  Google Scholar 

  14. C.H. Ooi, N.A.M. Isa, Quadrants dynamic histogram equalization for contrast enhancement. IEEE Trans. Consum. Electron. 56, 2552–2559 (2010)

    Article  Google Scholar 

  15. K. Singh, R. Kapoor, Image enhancement via median-mean based sub-ımage-clipped histogram equalization. Optik 125(17), 4646–4651 (2014)

    Google Scholar 

  16. S. Patra, R. Gautam, A. Singla, A novel context-sensitive multilevel thresholding for image segmentation. Appl Soft Comput J 23, 122–127 (2014). https://doi.org/10.1016/j.asoc.2014.06.016

    Article  Google Scholar 

  17. R. Dhaya, Improved image processing techniques for user immersion problem alleviation in virtual reality environments. J. Innov. Image Process. (JIIP) 2(02), 77–84 (2020)

    Article  Google Scholar 

  18. S. Dutta, A. Banerjee, Highly precise modified blue whale method framed by blending bat and local search algorithm for the optimality of image fusion algorithm. J. Soft Comput. ing Paradigm (JSCP) 2(04), 195–208 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Kakatiya Institute of Technology and Science, Warangal, India

    Rangu Srikanth, Koppisetti Lakshmi Sowmya, Sika Anjana, Gajarajula Vamshi & Annem Ram Mohan Reddy

Authors
  1. Rangu Srikanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koppisetti Lakshmi Sowmya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sika Anjana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gajarajula Vamshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Annem Ram Mohan Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rangu Srikanth .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, India

    G. Ranganathan

  2. Ryerson Communications Lab, Toronto, ON, Canada

    Xavier Fernando

  3. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Fuqian Shi

  4. Department of Computer Science, University of Sultan Moulay Slimane, Beni Mellal, Morocco

    Youssouf El Allioui

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Srikanth, R., Sowmya, K.L., Anjana, S., Vamshi, G., Reddy, A.R.M. (2022). Improved Image Enhancement of Natural Images with Median Mean-Based Sub-Image Clipped Histogram Equalization. In: Ranganathan, G., Fernando, X., Shi, F., El Allioui, Y. (eds) Soft Computing for Security Applications . Advances in Intelligent Systems and Computing, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-5301-8_61

Download citation

Publish with us

Policies and ethics

Search

Navigation