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Computational Intelligence in Data Mining pp 89–100Cite as

Estimation of Noise in Digital Image

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 556))

Abstract

Denoising plays an important role in improving the performance of algorithms such as classification, recognition, enhancement, segmentation, etc. To eliminate the noise from noisy image, one should know the noise type, noise level, noise distribution, etc. Typically noise level information is identified from noise standard deviation. Estimation of the image noise from the noisy image is major concern for several reasons. So, efficient and effective noise estimation technique is required to suppress the noise from the noisy image. This paper presents noise estimation based on rough fuzzy c-means clustering technique. The experimental results and performance analysis of the system are presented.

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References

  1. S.I. Olsen “Estimation of noise in images An evaluation”, Computer Vision Graphics Image Processing, Graphics Models. Vol. 55, No. 4, pp. 319–323, 1993.

    Google Scholar 

  2. Angelo Bosco, Arcangelo Bruna, Giuseppe Messina, Giuseppe Spampinato, “Fast Method for Noise Level Estimation and Integrated Noise Reduction”, IEEE Transaction on Consumer Electronics, vol. 51, No. 3, pp 1028–1033, August 2005.

    Google Scholar 

  3. Aishy Amer, and Eric. Dubois, “Reliable and Fast structure-Oriented Video Noise Estimation,” in Proc. IEEE International conference. Image Processing, Montreal Quebec, Canada, Sep 2002.

    Google Scholar 

  4. Jolliffe, I.T. “Principal Component Analysis” 2nd ed. 2002, XXIX.

    Google Scholar 

  5. D. Muresan and T. Parks, “Adaptive principal components and image denoising,” in Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on, vol. 1. IEEE, 2003.

    Google Scholar 

  6. T. Tasdizen, “Principal components for non-local means image denoising,” in Image Processing, ICIP 2008, 15th IEEE International Conference on. IEEE, 2008, pp. 1728–1731.

    Google Scholar 

  7. A. Foi, “Noise estimation and removal in MR imaging: The Variance Stabilization Approach,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on. IEEE, 2011, pp. 1809–1814.

    Google Scholar 

  8. Komstantionos Konstantiniders, Balas Natarajan, Gregory S. Yovanof “Noise Estimation and Filtering Using Block-Based Singular Value Decomposition” IEEE Transactions on Image Processing, Volume: 6, No: 3 Pp: 479–483, March 1997.

    Google Scholar 

  9. H. C. Andrews and C. L. Patterson, “Singular value decompositions and digital image processing,” IEEE Trans. Acoustic., Speech and Signal Processing, vol. ASSP-24, pp. 26–53, Feb. 1976.

    Google Scholar 

  10. Hoppner, F., et al., Fuzzy Cluster Analysis: methods for classification, data analysis, and image recognition. 1999, New York: John Wiley & Sons, Ltd.

    Google Scholar 

  11. Man, Y. and Gath, I., Detection and separation of ring shaped clusters using fuzzy clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1994. 16(8): pp. 855–861.

    Google Scholar 

  12. Gath, I. and Hoory, D., Fuzzy clustering of elliptic ring-shaped clusters. Pattern Recognition Letters, 1995. 16(7): pp. 727–741.

    Google Scholar 

  13. Ameer Ali, M., Dooley, L.S., and Karmakar, G.C., Object based segmentation using fuzzy clustering, in IEEE International Conference on Acoustics, Speech, and Signal Processing. 2006.

    Google Scholar 

  14. Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithm. 1981, New York: Plenum Press.

    Google Scholar 

  15. Fan, J.L., Zhen, W.Z., and Xie, W.X., Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Letters, 2003. 24: pp. 1607– 1612.

    Google Scholar 

  16. Krishnapuram, R. and Keller, J.M., A possibilistic approach to clustering. International Journal of Fuzzy Systems, 1993. 2(2): pp. 98–110.

    Google Scholar 

  17. Gustafson, D.E. and Kessel, W.C., Fuzzy clustering with a fuzzy covariance matrix. In Proceedings of IEEE Conference on Decision Control. 1979. pp. 761–766.

    Google Scholar 

  18. N.R. Pal and J.C. Bezdek, “On cluster validity for fuzzy C-Means model”, IEEE trans. Fuzzy Systems, 3(3), 1995, 370–379.

    Google Scholar 

  19. H.-C. Lee et al., “Digital image noise suppression method using SVD block transform,” U.S. Patent 5 010 504, April. 1991.

    Google Scholar 

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Authors and Affiliations

  1. B.V.B. College of Engineering and Technology, Hubballi, 31, India

    K. G. Karibasappa

  2. The Oxford College of Engineering, Bengaluru, India

    K. Karibasappa

Authors
  1. K. G. Karibasappa
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  2. K. Karibasappa
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Correspondence to K. G. Karibasappa .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of CSE, National Institute of Technology (NIT), Rourkela, Odisha, India

    Durga Prasad Mohapatra

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© 2017 Springer Nature Singapore Pte Ltd.

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Karibasappa, K.G., Karibasappa, K. (2017). Estimation of Noise in Digital Image. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_9

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  • DOI: https://doi.org/10.1007/978-981-10-3874-7_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3873-0

  • Online ISBN: 978-981-10-3874-7

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