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Performance Evaluation of De-noising Techniques Using Full-Reference Image Quality Metrics

  • Palwinder SinghEmail author
  • Leena Jain
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 805)

Abstract

The de-noising of digital images is crucial preprocessing step before moving toward image segmentation, representation and object recognition. It is an important to find out efficacy of filter for different noise models because filtering operation is application oriented task and performance varies according to type of noise present in images. A comparative study is made to elucidate the behavior of different spatial filtering techniques under different noise models. In this paper different types of noises like Gaussian noise, Speckle noise, Salt & Pepper noise is applied on grayscale standard image of Lenna and using spatial filtering techniques the values of full reference based image quality metrics are found and compared in tabular and graphical form. The outcome of comparative study shows that Lee, Kuan and Anisotropic Diffusion Filter worked well for Speckle noise, the Salt and Pepper noise has significantly reduced using Median and AWMF, and the Mean filter and Wiener filter works immensely efficient for reducing Gaussian noise.

Keywords

Spatial filter Additive noise Multiplicative noise Image quality metrics 

Notes

Acknowledgement

The authors express their sincere gratitude to I.K.G Punjab Technical University, kapurthala for their support and motivation.

References

  1. 1.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Thomson, Toronto (2008)Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Prentice Hall, Upper Saddle River (2008)Google Scholar
  3. 3.
    Pitas, I., Venetsanopoulos, A.N.: Nonlinear Digital Filters: Principles and Applications. The Springer International Series in Engineering and Computer Science. Springer, New York (1990).  https://doi.org/10.1007/978-1-4757-6017-0CrossRefzbMATHGoogle Scholar
  4. 4.
    Goodman, J.W.: Some fundamental properties of speckle. J. Opt. Soc. Am. 66(11), 1145–1150 (1976)CrossRefGoogle Scholar
  5. 5.
    Burckhardt, C.B.: Speckle in ultrasound B-mode scans. IEEE Trans. Sonics Ultrason. 25(1), 1–6 (1978)CrossRefGoogle Scholar
  6. 6.
    Ma, Q., Kaplan, D.: On the statistical characteristics of log-compressed Rayleigh signals: theoretical formulation and experimental results. J. Acoust. Soc. Am. 3, 1396–1400 (1994)Google Scholar
  7. 7.
    Motwani, M.C., Motwani, R.C., Harris, F.C., Gadiya, M.C.: Survey of image denoising techniques. In: Proceedings of Global Signal Processing, Santa Clara (2004)Google Scholar
  8. 8.
    Boncelet, C.: Image noise models. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing. Academic Press, Boston (2005)Google Scholar
  9. 9.
    Tukey, J.W.: Non linear methods for smoothing data. In: Proceeding of EASCON, p. 673 (1974)Google Scholar
  10. 10.
    Jayant, N.S.: Average and median based smoothing techniques for improving digital speech quality in the presence of transmission error. IEEE Trans. Commun. 24, 1043–1045 (1976)CrossRefGoogle Scholar
  11. 11.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall Information and System Sciences Series. Prentice-Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  12. 12.
    Pratt, W.K.: Digital Image Processing, 4th edn. Wiley, New York (2007)CrossRefGoogle Scholar
  13. 13.
    Kotropoulos, C., Pitas, I.: Optimum non linear signal detection and estimation in the presence of ultrasonic speckle. Ultrason. Imaging 14(3), 249–275 (1992)CrossRefGoogle Scholar
  14. 14.
    Karaman, M., Kutay, M.A., Bozdagi, G.: An adaptive speckle suppression filter for medical ultrasonic imaging. IEEE Trans. Med. Imaging 14(2), 283–292 (1992)CrossRefGoogle Scholar
  15. 15.
    Weickert, J.: Efficient and reliable schemes for non linear diffusion filtering. IEEE Trans. Image Process. 7(3), 398–410 (1998)CrossRefGoogle Scholar
  16. 16.
    Rankovic, N., Tuba, M.: Improved adaptive median filter for denoising ultrasound images. In: Proceedings of the 6th European Computing Conference, pp. 169–174 (2012)Google Scholar
  17. 17.
    Ataman, E., Wong, K.M., Aatre, B.K.: Some statistical properties of median filter. IEEE Trans. Acoust. Speech Sig. Process. 29, 1073–1075 (1981)CrossRefGoogle Scholar
  18. 18.
    Guan, L., Ward, R.: Restoration of randomly blurred images by the Wiener filter. IEEE Trans. Acoust. Speech Sig. Process. 37(4), 589–592 (1989)CrossRefGoogle Scholar
  19. 19.
    Kumar, S., Kumar, P.: Performance comparison of median and Wiener filter in image denoising. Int. J. Comput. Appl. 12(4), 27–31 (2010)Google Scholar
  20. 20.
    Lee, J.: Digital image enhancement and noise filtering using local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2, 165–168 (1980)CrossRefGoogle Scholar
  21. 21.
    Frost, V.S., Stiles, J.A., Holtzman, J.C., Shanmugam, K.S.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 157–166 (1982)CrossRefGoogle Scholar
  22. 22.
    Kuan, D.T., Sawchuk, A.A., Chavel, P., Strand, T.C.: Adaptive noise smoothing filter for images with signal dependent noise. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985)CrossRefGoogle Scholar
  23. 23.
    Malik, J., Perona, P.: Scale space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)CrossRefGoogle Scholar
  24. 24.
    Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11, 1260–1270 (2002)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wang, Z., Sheikh, H.R., Bovik, A.C.: Objective video quality assessment, Chap. 41. In: The Handbook of Video Databases: Design and Applications, Laboratory of Image and Video Engineering, The University of Texas, Austin, pp. 1041–1078. CRC Press (2003)Google Scholar
  26. 26.
    Pappas, T.N., Safranck, R.J.: Perceptual criteria for image quality evaluation. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing. Academic Press, Boston (2000)Google Scholar
  27. 27.
    Wang, Z., Bovik, A.C.: Mean square error, love it or leave it. IEEE Sig. Process. Mag. (2009).  https://doi.org/10.1109/msp2008-930648
  28. 28.
    Thung, K.H., Raveendran, P.: A survey of image quality measures. In: IEEE International Conference for Technical Postgraduates, pp. 1–4 (2009)Google Scholar
  29. 29.
    Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performances. IEEE Trans. Commun. 43(12), 2959–2965 (1995)CrossRefGoogle Scholar
  30. 30.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Sig. Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  31. 31.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.I.K.G Punjab Technical UniversityKapurthalaIndia
  2. 2.Global Institute of Management and Emerging TechnologiesAmritsarIndia

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