De-noising Method in the Wavelet Packets Domain for Phase Images

  • Juan V. Lorenzo-Ginori
  • Héctor Cruz-Enriquez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

Complex images contaminated by noise appear in various applications. To improve these phase images, noise effects, as loss of contrast and phase residues that deteriorate the phase unwrapping process, should be reduced. Noise reduction in complex images has been addressed by various methods, most of them dealing only with the magnitude image. Few works have been devoted to phase image de-noising, despite the existence of important applications like Interferometric Synthetic Aperture Radar (IFSAR), Current Density Imaging (CDI) and Magnetic Resonance Imaging (MRI). In this work, several de-noising algorithms in the wavelet packets domain were applied to complex images to recover the phase information. These filtering algorithms were applied to simulated images contaminated by three different noise models, including mixtures of Gaussian and Impulsive noise. Significant improvements in SNR for low initial values (SNR<5 dB) were achieved by using the proposed filters, in comparison to other methods reported in the literature.

Keywords

Phase Image Noise Model Wavelet Packet Simulated Image Normalize Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Lorenzo-Ginori, J.V., Plataniotis, K.N., Venetsanopoulos, A.N.: Non linear filtering for phase image de-noising. IEE Proc.-Vis. Image Signal Process 49(5), 290–296 (2002)CrossRefGoogle Scholar
  2. 2.
    Alexander, M.E., Baumgartner, R., Summers, A.R., Windischberger, C., Klarhoefer, M., Moser, E., Somorjai, R.L.: A Wavelet-based Method for Improving Signal-to-noise Ratio and Contrast in MR Images. Magnetic Resonance Imaging 18, 169–180 (2000)CrossRefGoogle Scholar
  3. 3.
    Nowak, R.D.: Wavelet-Based Rician Noise Removal for Magnetic Resonance Imaging. IEEE Transactions on Image Processing 8(10), 1408–1419 (1999)CrossRefGoogle Scholar
  4. 4.
    Braunisch, H., Bae-ian, W., Kong, J.A.: Phase unwrapping of SAR interferograms after wavelet de-noising. In: IEEE Geoscience and Remote Sensing Symposium, IGARSS 2000, vol. 2, pp. 752–754 (2000)Google Scholar
  5. 5.
    Zaroubi, S., Goelman, G.: Complex De-noising of MR Data Via Wavelet Analysis: Application to Functional MRI. Magnetic Resonance Imaging 18, 59–68 (2000)CrossRefGoogle Scholar
  6. 6.
    Misiti, M., et al.: Wavelet Toolbox user’s guide. The MathWorks Inc., Natick (2000)Google Scholar
  7. 7.
    Cruz-Enríquez, H., Lorenzo-Ginori, J.V.: Wavelet-based methods for improving signal-to-noise ratio in phase images. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 247–254. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Juan V. Lorenzo-Ginori
    • 1
  • Héctor Cruz-Enriquez
    • 1
  1. 1.Center for Studies on Electronics and Information TechnologiesUniversidad Central “Marta Abreu” de Las VillasSanta ClaraCuba

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