http://iet.metastore.ingenta.com
1887

Enhancement of dark and low-contrast images using dynamic stochastic resonance

Enhancement of dark and low-contrast images using dynamic 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.

In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

References

    1. 1)
      • 1. Gonzales, R.C., Woods, E.: ‘Digital image processing’ (Addison-Wesley, Reading, MA, 1992).
    2. 2)
      • 2. Lim, J.S.: ‘Two-dimensional signal and image processing’ (Prentice-Hall, Englewood Cliffs, NJ, 1990).
    3. 3)
      • 3. Jobson, D., Rahman, Z., Woodell, G.: ‘Properties and performance of a center/surround retinex’, IEEE Trans. Image Process., 1997, 6, (3), pp. 451462 (doi: 10.1109/83.557356).
    4. 4)
      • 4. Wolf, S., Ginosar, R., Zeevi, Y.: ‘Spatio-chromatic image enhancement based on a model of human visual information system’, J. Vis. Commun. Image Represent., 1998, 9, (1), pp. 2537 (doi: 10.1006/jvci.1998.0371).
    5. 5)
      • 5. Benzi, R., Sutera, A., Vulpiani, A.: ‘The mechanism of stochastic resonance’, J. Phys. A, 1981, 14, pp. L453L457 (doi: 10.1088/0305-4470/14/11/006).
    6. 6)
      • 6. Bulsara, A.R., Gammaitoni, L.: ‘Tuning in to noise’, Phys. Today, 1996, 49, pp. 3945 (doi: 10.1063/1.881491).
    7. 7)
      • 7. Gammaitoni, L., Hanggi, P., Jung, P., Marchesoni, F.: ‘Stochastic resonance’, Rev. Mod. Phys., 1998, 70, pp. 223270 (doi: 10.1103/RevModPhys.70.223).
    8. 8)
      • 8. Hongler, M., Meneses, Y., Beyeler, A., Jacot, J.: ‘Resonant retina: exploiting vibration noise to optimally detect edges in an image’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (9), pp. 10511062 (doi: 10.1109/TPAMI.2003.1227982).
    9. 9)
      • 9. Ye, Q., Huang, H., He, X., Zhang, C.: ‘A SR-based radon transform to extract weak lines from noise images’. Proc. IEEE Int. Conf. on Image Processing, Barcelona, Spain, 2003, vol. 5, no. 6, pp. 18491852.
    10. 10)
      • 10. Ye, Q., Huang, H., Zhang, C.: ‘Image enhancement using stochastic resonance’. Proc. IEEE Int. Conf. Image Processing, Singapore, 2004, vol. 1, pp. 263266.
    11. 11)
      • 11. Peng, R., Chen, H., Varshney, P.K.: ‘Stochastic resonance: an approach for enhanced medical image processing’. IEEE/NIH Life Science Systems and Applications Workshop, 2007, vol. 1, pp. 253256.
    12. 12)
      • 12. Rallabandi, V.P.S.: ‘Enhancement of ultrasound images using stochastic resonance based wavelet transform’, Comput. Med. Imaging Graph., 2008, 32, pp. 316320 (doi: 10.1016/j.compmedimag.2008.02.001).
    13. 13)
      • 13. Rallabandi, V.P.S., Roy, P.K.: ‘Magnetic resonance image enhancement using stochastic resonance in Fourier domain’, Comput. Med. Imaging Graph., 2010, 28, pp. 13611373.
    14. 14)
      • 14. Ryu, C., Konga, S.G., Kimb, H.: ‘Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance’, Pattern Recognit. Lett., 2011, 32, (2), pp. 107113 (doi: 10.1016/j.patrec.2010.09.008).
    15. 15)
      • 15. Simonotto, E., Riani, M., Charles, S., Roberts, M., Twitty, J., Moss, F.: ‘Visual perception of stochastic resonance’, Phys. Rev. Lett., 1997, 78, (6), pp. 11861189 (doi: 10.1103/PhysRevLett.78.1186).
    16. 16)
      • 16. Piana, M., Canfora, M., Riani, M.: ‘Role of noise in image processing by the human perceptive system’, Phys. Rev. E, 2000, 62, (1), pp. 11041109 (doi: 10.1103/PhysRevE.62.1104).
    17. 17)
      • 17. McNamara, B., Wiesenfeld, K.: ‘Theory of stochastic resonance’, Phys. Rev. A, 1989, 39, (9), pp. 48544869 (doi: 10.1103/PhysRevA.39.4854).
    18. 18)
      • 18. Jha, R.K., Biswas, P.K., Chatterji, B.N.: ‘Contrast enhancement of dark images using stochastic resonance’, IET Image Process., IEE, 2012, 6, (3), pp. 230237 (doi: 10.1049/iet-ipr.2010.0392).
    19. 19)
      • 19. Benzi, R., Parisi, G., Sutera, A., Vulpiani, A.: ‘Stochastic resonance in climate change’, Tellus, 1982, 34, pp. 1016 (doi: 10.1111/j.2153-3490.1982.tb01787.x).
    20. 20)
      • 20. Rouvas-Nicolis, C., Nicolis, G.: ‘Stochastic resonance’, Scholarpedia, 2007, 2, (11), pp. 1474 (doi: 10.4249/scholarpedia.1474).
    21. 21)
      • 21. Risken, H.: ‘The Fokkar Plank equation’ (Springer Verlag, Berlin, 1984).
    22. 22)
      • 22. McDonnell, M.D., Stocks, N.G., Pearce, C.E.M., Abbott, D.: ‘Stochastic resonance: from suprathreshold stochastic resonance to stochastic signal quantization’ (Cambridge University Press, New York, 1990).
    23. 23)
      • 23. Jung, P., Hanggi, P.: ‘Amplification of small signal via stochastic resonance’, Phys. Rev. A, 1991, 44, (12), pp. 80328042 (doi: 10.1103/PhysRevA.44.8032).
    24. 24)
      • 24. Gard, T.C.: ‘Introduction to stochastic differential equations’ (Marcel-Dekker, New York, 1998).
    25. 25)
      • 25. Wang, Z., Sheikh, H.R., Bovik, A.C.: ‘No-reference perceptual quality assessment of jpeg compressed images’. Proc. IEEE Int. Conf. on Image Processing, New York, USA, 2002, vol. 1, pp. 477480.
    26. 26)
      • 26. http://www.facweb.iitkgp.emet.in/~jay/CES/README.html (accessed 7th July 2011).
    27. 27)
      • 27. Mukherjee, J., Mitra, S.K.: ‘Enhancement of color images by scaling the dct coefficients’, IEEE Trans. Image Process., 2008, 17, (10), pp. 17831794 (doi: 10.1109/TIP.2008.2002826).
    28. 28)
      • 28. Susstrunk, S., Winkler, S.: ‘Color image quality on the internet’. Proc. IS&T/SPIE Electronic Imaging: Internet Imaging V, 2004, vol. 5304, pp. 118131.
    29. 29)
      • 29. Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: ‘Edge-preserving decompositions for multi-scale tone and detail manipulation’, ACM Trans. Graph., 2008, 27, (3), pp. 110 (doi: 10.1145/1360612.1360666).
    30. 30)
      • 30. Zuiderveld, K.: ‘Contrast limited adaptive histogram equalization’ (Academic Press Professional Inc., San Diego, CA, USA, 1994), pp. 474485, http://portal.acm.org/citation.cfm?id = 180895.180940.
    31. 31)
      • 31. 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 Process., 1997, 6, (7), pp. 965976 (doi: 10.1109/83.597272).
    32. 32)
      • 32. Yang, C.: ‘Image enhancement by the modified high-pass filtering approach’, Optik – Int. J. Light Electron. Opt., 2009, 120, (17), pp. 886889 (doi: 10.1016/j.ijleo.2008.03.016).
    33. 33)
      • 33. http://www.cs.huji.ac.il/_danix/epd/ (accessed 15th August 2011).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2012.0114
Loading

Related content

content/journals/10.1049/iet-ipr.2012.0114
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address