Skip to main content
SpringerLink
Log in

Enhancement of low-contrast images by internal noise-induced Fourier coefficient rooting

Signal, Image and Video Processing volume 9, pages 255–263 (2015)Cite this article

Abstract

This paper presents a study of noise-enhanced iterative processing on Fourier coefficients for enhancement of low-contrast images. The processing equation is derived from the concept of dynamic stochastic resonance (SR), where the presence of optimum amount of noise produces an improved performance in the system. Similar to our earlier works on SR-based contrast enhancement, noise in the current context is the internal noise inherent in an image due to insufficient illumination. Here, however, the parameter selection is done so as to achieve large noise suppression. Iteration is terminated when target performance has been achieved. It is observed that the increase in the variance of the Fourier magnitude distribution leads to an increase in the contrast of the image. The increase in the variance is analytically proven to be equivalent to the process of coefficient rooting. Comparison has been made with various state-of-the-art SR and non-SR-based techniques in spatial/frequency domains. The proposed technique has been found to give noteworthy performance for both low-contrast and dark images among the SR-based techniques. The performance is also found to be better than most of the non-SR-based techniques, in terms of contrast enhancement, perceptual quality and colorfulness.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

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

References

  1. Aghagolzadeh, S., Ersoy, O.K.: Transform image enhancement. Opt. Eng. 31, 614–626 (1992)

    Article  Google Scholar 

  2. Celik, T., Tjahjadi, T.: Contextual and variational contrast enhancement. IEEE Trans. Image Process. 20(12), 3431–3441 (2011)

    Article  MathSciNet  Google Scholar 

  3. Cherifi, D., Beghdadi, A., Belbachir, A.H.: Color contrast enhancement method using steerable pyramid transform. SIViP 4, 247–262 (2010)

    Article  MATH  Google Scholar 

  4. Chouhan, R., Biswas, P.K.: Dynamic range compression using hvs-based segmentation in a modified noise-aided image enhancement model. In: Proceedings of IEEE International Conference on Image Processing, pp. 4532–4536. Paris, France (2014)

  5. Chouhan, R., Jha, R.K., Biswas, P.K.: Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance. In: Proceedings of Indian Conference on Computer Vision, Graphics and Image Processing, pp. 73:1–73:8. Mumbai, India (2012)

  6. Chouhan, R., Jha, R.K., Biswas, P.K.: Enhancement of dark and low-contrast images using dynamic stochastic resonance. IET Image Proc. 7(2), 174–184 (2013)

    Article  MathSciNet  Google Scholar 

  7. Chouhan, R., Jha, R.K., Biswas, P.K.: Noise-enhanced contrast stretching of dark images in svd-dwt domain. In: Proceedings of 2013 Annual IEEE India Conference (INDICON). Mumbai, India (2013). doi:10.1109/INDCON.2013.6726000

  8. Erkelens, J.S., Hendriks, R.C., Heusdens, R.: On the estimation of complex speech dft coefficients without assuming independent real and imaginary parts. IEEE Signal Process. Lett. 15, 213 (2008)

    Article  Google Scholar 

  9. Hasikin, K., Isa, N.A.M.: Adaptive fuzzy intensity measure enhancement technique for non-uniform illumination and low-contrast images. Signal Image Video Process. (2013). doi:10.1007/s11760-013-0596-1

  10. 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. 25(9), 1051–1062 (2003)

    Article  Google Scholar 

  11. Jha, R.K., Chouhan, R.: Noise-induced contrast enhancement using stochastic resonance on singular values. Signal Image Video Process. 8(2), 339–347 (2014)

    Article  Google Scholar 

  12. Jha, R.K., Chouhan, R., Aizawa, K., Biswas, P.K.: Dark and low-contrast image enhancement using dynamic stochastic resonance in dct domain. APSIPA Trans. Signal Inf. Process. 2, e6 (2013). doi:10.1017/ATSIP.2013.7

    Article  Google Scholar 

  13. Jha, R.K., Chouhan, R., Biswas, P., Aizawa, K.: Internal noise-induced contrast enhancement of dark images. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 973–976. Orlando, FL, USA (2012)

  14. Jha, R.K., Chouhan, R., Biswas, P.K.: Noise-induced contrast enhancement of dark images using non-dynamic stochastic resonance. In: Proceedings of National Conference on Communications, pp. 1–5 (2012). doi:10.1109/NCC.2012.6176793

  15. 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. 6(7), 965–976 (1997)

    Article  Google Scholar 

  16. Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)

    Article  Google Scholar 

  17. Lim, S.H., Isa, N.A.M., Ooi, C.H., Toh, K.K.V.: A new histogram equalization method for digital image enhancement and brightness preservation. Signal Image Video Process. (2013). doi:10.1007/s11760-013-0500-z

  18. Liu, Z., Laganire, R.: Context enhancement through infrared vision: a modified fusion scheme. SIViP 1, 293–301 (2007)

    Article  MATH  Google Scholar 

  19. 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)

    Google Scholar 

  20. Mukherjee, J.: http://www.facweb.iitkgp.ernet.in/~jay/CES/README.html (2008). Accessed on 7 July 2011

  21. Mukherjee, J., Mitra, S.K.: Enhancement of color images by scaling the dct coefficients. IEEE Trans. Image Process. 17(10), 1783–1794 (2008)

    Article  MathSciNet  Google Scholar 

  22. Ozcinar, C., Demirel, H., Anbarjafari, G.: Image equalization using singular value decomposition and discrete wavelet transform. Discrete wavelet transforms—theory and applications (2011). ISBN: 978-953-307-185-5, InTech

  23. Peng, R., Chen, H., Varshney, P.K.: Stochastic resonance: an approach for enhanced medical image processing. IEEE/NIH Life Science Systems and Applications Workshop, vol. 1, 253–256 (2007)

  24. Rallabandi, V.P.S.: Enhancement of ultrasound images using stochastic resonance based wavelet transform. Comput. Med. Imaging Graph. 32, 316–320 (2008)

    Article  Google Scholar 

  25. Rallabandi, V.P.S., Roy, P.K.: Magnetic resonance image enhancement using stochastic resonance in fourier domain. Magn. Reson. Imaging 28, 1361–1373 (2010)

    Article  Google Scholar 

  26. Rivera, A.R., Ryu, B., Chae, O.: Content-aware dark image enhancement through channel division. IEEE Trans. Image Process. 21(9), 3967–3980 (2012)

    Article  MathSciNet  Google Scholar 

  27. Ryu, C., Konga, S.G., Kimb, H.: Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance. Pattern Recogn. Lett. 32(2), 107–113 (2011)

    Article  Google Scholar 

  28. Santhi, K., Banu, R.S.D.: Contrast enhancement by modified octagon histogram equalization. Signal Image Video Process. (2014). doi:10.1007/s11760-014-0643-6

  29. Simonotto, E., Riani, M., Charles, S., Roberts, M., Twitty, J., Moss, F.: Visual perception of stochastic resonance. Phys. Rev. Lett. 78(6), 1186–1189 (1997)

    Article  Google Scholar 

  30. Tang, J., Peli, E., Acton, S.: Image enhancement using a contrast measure in the compressed domain. IEEE Signal Process. Lett. 10(10), 289–292 (2003)

    Article  Google Scholar 

  31. Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of jpeg compressed images. In: Proceedings of IEEE International Conference on Image Processing, vol. 1, pp. 477–480 (2002)

  32. Yang, C.: Image enhancement by the modified high-pass filtering approach. Optik Int. J. Light Electron Optics 120(17), 886–889 (2009)

    Article  Google Scholar 

  33. Ye, Q., Huang, H., He, X., Zhang, C.: A SR-based radon transform to extract weak lines from noise images. In: Proceedings of IEEE International Conference on Image Processing (ICIP), vol. 5, pp. 1849–1852 (2003)

  34. Ye, Q., Huang, H., He, X., Zhang, C.: Image enhancement using stochastic resonance. In: Proceedings of IEEE International Conference on Image Processing, vol. 1, pp. 263–266 (2004)

  35. Zolfaghari, P., Robinson, T.: Formant analysis using mixtures of gaussians. In: Proceedings of International Conference on Spoken Language, ICSLP, vol. 2, pp. 1229–1232 (1996)

Download references

Acknowledgments

The authors would like to thank Mr. Sajan Pillai of IIT Bombay for providing the test images. The authors also acknowledge the contributions of James Paul and Satish Meena, along with Arnab Mitra and Raghavendra Reddy of IIT Kharagpur, for providing implementations of the CACD and CVC algorithms, respectively.

Author information

Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Rajlaxmi Chouhan & P. K. Biswas

  2. Indian Institute of Technology Patna, Patna, India

    R. K. Jha

Authors
  1. Rajlaxmi Chouhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. K. Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. K. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajlaxmi Chouhan.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 13080 KB)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chouhan, R., Biswas, P.K. & Jha, R.K. Enhancement of low-contrast images by internal noise-induced Fourier coefficient rooting. SIViP 9 (Suppl 1), 255–263 (2015). https://doi.org/10.1007/s11760-015-0812-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-015-0812-2

Keywords

search

Navigation