Skip to main content
SpringerLink
Log in

PSO-based stochastic resonance for automatic contrast enhancement of images

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Images suffer from poor contrast due to insufficient illumination. The dynamic range of pixel intensities in low-contrast images is very limited, thus not revealing the complete details in the image. Contrast enhancement techniques are used to improve the visual quality of such low-contrast images. Many blind algorithms have been developed to enhance the image without understanding whether it requires such an enhancement procedure. In this paper, we present an automatic contrast enhancement method where the need of the contrast enhancement is ascertained by modelling image contrast factor and using log-likelihood function. Once the need is identified, particle swarm optimization-based stochastic resonance is applied to improve the contrast of the image. The proposed method significantly improves the contrast and simultaneously preserves the brightness of the low-contrast images without introducing any artefacts. Quantitative evaluation of a comparative experimentation with a few conventional enhancement methods demonstrates that the proposed method achieves better-quality results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

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

  2. Ketcham, D.J., Lowe R., Weber W.: Real Time Image Enhancement Techniques. Seminar on Image Processing. Hughes Aircrafts, pp. 1–6 (1996)

  3. Land, E. H.: The Retinex theory for color vision. Scientific American published by W.H. Freeman and company 660 market street, San Francisco, California. vol. 237. No 6, pp. 108–128 (2007)

  4. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multi-scale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Proces. 6(7), 965–976 (1997). doi:10.1109/83.597272

    Article  Google Scholar 

  5. Jain, A.K.: Fundamentals of Digital Image Processing. Englewood Cliffs, Prentice Hall (1989)

    MATH  Google Scholar 

  6. Kwok, N.M., Ha, Q.P., Liu, D., Fang, G.: Contrast enhancement and intensity preservation for gray-level images using multi-objective particle swarm optimization. IEEE Trans. Autom. Sci. Eng. 6(1), 145–155 (2009). doi:10.1109/TASE.2008.917053

    Article  Google Scholar 

  7. Cherifi, D., Beghdadi, A., Belbachir, A.H.: Color contrast enhancement method using steerable pyramid transform. Signal Image Video Proces. 4(2), 247–262 (2010). doi:10.1007/s11760-009-0115-6

    Article  MATH  Google Scholar 

  8. Benzi, R., Sutera, S., Vulpiani, A.: The mechanism of stochastic resonance. J. Phys. A Math. General 14(11), L453–L457 (1981). doi:10.1088/0305-4470/14/11/006

  9. Miyamoto, R.T., et al.: Stochastic resonance of a threshold detector: image visualization and explanation. Proc. IEEE Int. Symp. Circuit Syst. (ISCAS) 4, IV-521–V-523 (2002). doi:10.1109/ISCAS.2002.1010507

    Google Scholar 

  10. Vaudelle, F., Gazengel, J., Rivoire, G., Godivier, X., Chapeau-Blondeau, F.: Stochastic resonance and noise-enhanced transmission of spatial signals in optics: the case of scattering. J. Optic. Soc. Am. B 15(11), 2674–2680 (1998)

    Article  Google Scholar 

  11. Ye, Q., Huang, H., Zhang, C.: Image enhancement using stochastic resonance. ICIP 1, 263–266 (2004). doi:10.1109/ICIP.2004.1418740

    Google Scholar 

  12. Jha, R.K., Biswas, P.K., Chatterji, B.N.: Enhancement of digital images using stochastic resonance. TENCON, 1–6 (2005). doi:10.1109/TENCON.2005.301203

  13. Chouhan, R., Kumar, C.P., Kumar, R., Jha, R.K.: Contrast enhancement of dark images using stochastic resonance in wavelet domain. Int. J. Mach. Learn. Comput., 2(5) (2012). doi:10.7763/IJMLC.2012.V2.220

  14. Mansoor Roomi, S.M., Karthikeyan, J.P., Shankar, R.: A contrast enhancement based on visual significance. J. Indian Inst. Sci. 79, 89–97 (1999)

  15. Stormont, D.P.: An online Bayesian classifier for object identification. IEEE Int. Workshop Secur. Rescue Robot. Safety, 1–5 (2007). doi:10.1109/SSRR.2007.4381283

  16. Benzi, R., Parisi, G., Sutera, A., Vulpiani, A.: Stochastic resonance in climatic change. Tellus 34, 10–16 (1982)

    Article  Google Scholar 

  17. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys. Man. Cyber. 9(1), 6266 (1979). doi:10.1109/TSMC.1979.4310076

    MathSciNet  Google Scholar 

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995). doi:10.1109/ICNN.1995.488968

    Article  Google Scholar 

  19. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, ISBN: 978-0-471-05669-0 (2001)

  20. Zuiderveld, K.: Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV. Academic Press Professional, San Diego (1994)

  21. Braik, M., Sheta, A., Ayesh, A.: Image enhancement using particle swarm optimization. In: Proceedings of the world congress on engineering (WCE), vol.I, ISBN:978-988-98671-5-7 (2007)

  22. Maragatham, G., Mansoor Roomi, S.M.: An automatic contrast enhancement method based on stochastic resonance. ICCCNT. 1–7 (2013). doi:10.1109/ICCCNT.2013.6726602

  23. Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consum. Electron. 51(4), 1326–1334 (2005)

    Article  MathSciNet  Google Scholar 

  24. Maragatham, G., Mansoor Roomi, S. M, Manoj Prabu, T.: Contrast enhancement by object based histogram equalization. WICT, 1118–1122 (2011)

  25. Jha, R.K., Chouhan, R.: Noise-induced contrast enhancement using stochastic resonance on singular values. SIVP 8, 339–347 (2012)

    Google Scholar 

  26. Hashemi, S., Kiani, S., Noroozi, N., Ebrahimi, M.M.: An image contrast enhancement method based on genetic algorithm. Pattern Recognit. Lett. 31, 18161824 (2010)

    Article  Google Scholar 

  27. Draa, A., Bouaziz, A.: An Artificial Bee Colony Algorithm for Image Contrast Enhancement, vol. 16. Elsevier, Amsterdam (2014). doi:10.1016/j.swevo.2014.01.003

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication, Anna University-UCE - Ramnad Campus, Ramnad, India

    G. Maragatham

  2. Department of Electronics and Communication, Thiagarajar College of Engineering, Madurai-15, India

    S. Md. Mansoor Roomi

Authors
  1. G. Maragatham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Md. Mansoor Roomi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Maragatham.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maragatham, G., Roomi, S.M.M. PSO-based stochastic resonance for automatic contrast enhancement of images. SIViP 10, 207–214 (2016). https://doi.org/10.1007/s11760-014-0728-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0728-2

Keywords

Search

Navigation