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A Fast Scheme for Multiscale Signal Denoising

  • Conference paper
Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

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Abstract

This paper exploits the time scale structure of the wavelet coefficients for implementing a novel and fast scheme for signal and image denoising. The time scale behavior of the coefficients is rigorously modeled through superposition of simple atoms using suitable projection spaces. This result allows us to avoid expensive numerical schemes requiring a low computational effort. Extensive experimental results show the competitive performances of the proposed approach.

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References

  1. Balster, E.J., Zheng, Y.F., Ewing, R.L.: Feature-Based Wavelet Shrinkage Algorithm for image Denoising. IEEE Trans. on Image Proc. 14(12) (December 2005)

    Google Scholar 

  2. Bruni, V., Vitulano, D.: Wavelet based Signal Denoising via Simple Singularities Approximation. Signal Processing 86, 859–876 (2006), http://www.iac.rm.cnr.it

    Article  Google Scholar 

  3. Bruni, V., Piccoli, B., Vitulano, D.: Scale space atoms for signals and image denoising, IAC Report (2006)

    Google Scholar 

  4. Chang, S.G., Yu, B., Vetterli, M.: Spatially Adaptive Thresholding with Context Modeling for Image Denoising. IEEE Trans. on Image Proc. 9(9), 1522–1530 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  5. Donoho, D.L.: Denoising by soft thresholding. IEEE Trans. on Inf. Theory 41(3), 613–627 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  6. Dragotti, P.L., Vetterli, M.: Wavelet Footprints: Theory, Algorithms and Applications. IEEE Trans. on Signal Proc. 51(5), 1306–1323 (2003)

    Article  MathSciNet  Google Scholar 

  7. Fan, G., Xia, X.: Image Denoising using a Local Contextual Hidden Markov Model in the Wavelet Domain. IEEE Signal Proc. Lett. 8(5), 125–128 (2001)

    Article  MathSciNet  Google Scholar 

  8. Kervrann, C., Boulanger, J.: Unsupervised Patch-Based Image Regularization and Representation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 555–567. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Koenderink, J.: The structure of images. Biol. Cybern. 50, 363–370 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  10. Mallat, S., Hwang, W.L.: Singularity Detection and Processing with Wavelets. IEEE Trans. on Inf. Theory 38(2) (March 1992)

    Google Scholar 

  11. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, London (1998)

    MATH  Google Scholar 

  12. Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. on PAMI 12, 629–639 (1990)

    Google Scholar 

  13. Pizurica, A., Philips, W., Lemanhieu, I., Acheroy, M.: A Joint Inter- and Intrascale Statistical Model for Bayesian Wavelet Based Image Denoising. IEEE Trans. on Image Proc. 11(5) (May 2002)

    Google Scholar 

  14. Portilla, J., Strela, V., Wainwright, M., Simoncelli, E.: Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain. IEEE Trans. on Image Proc. 12(11), 1338–1351 (2003)

    Article  MathSciNet  Google Scholar 

  15. Shih, A.C., Liao, H.M., Lu, C.: A New Iterated Two-Band Diffusion Equation: Theory and Its Application. IEEE Trans. on Image Proc. 12(4), 466–476 (2003)

    Article  MathSciNet  Google Scholar 

  16. Sendur, L., Selesnick, I.W.: Bivariate Shrinkage with Local Variance Estimation. IEEE Signal Proc. Letters 9(12) (December 2002)

    Google Scholar 

  17. Teboul, S., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Variational Approach for Edge-Preserving Regularization Using Coupled PDE’s. IEEE Trans. on Image Proc. 7(3), 387–397 (1998)

    Article  Google Scholar 

  18. Mrazek, P., Weickert, J., Steidl, G.: Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising. Int. Journal of Computer Vision 64(2/3), 171–186 (2005)

    Article  Google Scholar 

  19. Witkin, A.: Scale-space filtering. In: International Joint Conf. Artificial Intelligence, Karlsruhe, West Germany, pp. 1019–1021 (1983)

    Google Scholar 

  20. Pizurica, A., Philips, W.: Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising. IEEE Trans. on Image Proc (2007)

    Google Scholar 

  21. Luisier, F., Blu, T., Unser, M.: A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding. IEEE Trans. on Image Proc. 16(3) (March 2007)

    Google Scholar 

  22. Elad, M., Aharon, M.: Image Denoising via Learned Dictionaries and Sparse Representation. In: Proc. of IEEE CVPR 2006 (2006)

    Google Scholar 

  23. Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise Shape Adaptive DCT for High Quality Denoising and Deblocking of Grayscale and Color Images. IEEE Trans. on Image Proc. 16(5) (May 2007)

    Google Scholar 

  24. Bruni, V., Piccoli, B., Vitulano, D.: Wavelet time-scale dependencies for signal and image compression. In: Proc. of IEEE ISPA, Zagreb, 2005, pp.105–110 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Istituto per le Applicazioni del Calcolo “M. Picone”, C. N. R., Viale del Policlinico 137, 00161, Rome, Italy

    Vittoria Bruni, Benedetto Piccoli & Domenico Vitulano

Authors
  1. Vittoria Bruni
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  2. Benedetto Piccoli
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  3. Domenico Vitulano
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Editor information

Aurélio Campilho Mohamed Kamel

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© 2008 Springer-Verlag Berlin Heidelberg

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Bruni, V., Piccoli, B., Vitulano, D. (2008). A Fast Scheme for Multiscale Signal Denoising. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_3

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  • DOI: https://doi.org/10.1007/978-3-540-69812-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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