Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Contrast enhancement of dark images using stochastic resonance

Contrast enhancement of dark images using stochastic resonance

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Two stochastic resonance (SR)-based techniques are introduced for enhancement of very low-contrast images. In the proposed SR-based image enhancement technique-1, an expression for optimum threshold has been derived. Gaussian noise of increasing standard deviation has been added iteratively to the low-contrast image until the quality of enhanced image reaches maximum. A quantitative parameter ‘distribution separation measure (DSM)’ is used to measure the enhancement quality. In order to reduce the required number of iterations in the second enhancement technique the author's have derived an expression for optimum noise standard deviation σoptimum that maximises signal-to-noise ratio (SNR). Image enhancement is obtained by iterating only with few noise standard deviations around σoptimum. This reduces number of iterations drastically. Comparison with the existing methods shows the superiority of the proposed method.

References

    1. 1)
      • R.C. Gonzales , R.E. Woods . (1993) Digital image processing.
    2. 2)
      • S.O. Rice . The theory of random noise. Bell Syst. Tech. J.
    3. 3)
    4. 4)
      • Ye, Q., Huang, H., He, X., Zhang, C.: `A SR-based Radon transform to extract weak lines from noise images', Proc. IEEE ICIP, 2003, 5, p. 1849–1852.
    5. 5)
    6. 6)
    7. 7)
      • T.J. Brown . An adaptive strategy for wavelet based image enhancement. Proc. IMVIP , 67 - 81
    8. 8)
    9. 9)
      • Jha, R.K., Biswas, P.K., Chatterji, B.N.: `Contrast enhancement of digital images using stochastic resonance', Proc. Tencon-2005, IEEE Region 10 Conf., 2005, Melbourne, Australia, 1, p. 1–6.
    10. 10)
      • Ye, Q., Huang, H., He, X., Zhang, C.: `Image enhancement using stochastic resonance', Proc. IEEE ICIP, 2004, 1, p. 263–266.
    11. 11)
    12. 12)
      • Jha, R.K., Biswas, P.K., Chatterji, B.N.: `Image denoising using stochastic resonance', Proc. ICCR, 2006, p. 320–325.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • J.S. Lim . (1990) Two-dimensional signal and image processing.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • A. Laine , J. Fan , S. Sculer . (1994) A framework for contrast enhancement by dyadic wavelet analysis, digital mammography.
    23. 23)
      • Z. Gingl , L.B. Kiss , F. Moss . Non dynamical stochastic resonance: theory and experiments with white and various colored noises. Luglio-Agosto , 7 , 795 - 802
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2010.0392
Loading

Related content

content/journals/10.1049/iet-ipr.2010.0392
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address