Commenced in January 2007
Paper Count: 31532
Kalman-s Shrinkage for Wavelet-Based Despeckling of SAR Images
Abstract:In this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman-s filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman-s filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients. Experimental results demonstrate that our method compares favorably to several other despeckling methods on test synthetic aperture radar (SAR) images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330507Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1353
 F. Abramovich and Y. Benjamini, "Adaptive thresholding of wavelet coefficients," Comput. Statist. Data Anal., vol. 22, pp. 351-361, 1996.
 F. Abramovich, T. Sapatinas, and B. Silverman, "Wavelet thresholding via a Bayesian approach," J. R. Stat., vol. 60, pp. 725- 749, 1998.
 Z. Cai, T. H. Cheng, C. Lu, and K. R. Subramanian, "Efficient wavelet based image denoising algorithm," Electron Lett., vol. 37, no. 11, pp. 683-685, May 2001.
 S. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans. Image Processing, vol. 9, pp. 1532-1546, Sept. 2000.
 S. Chang, B. Yu, and M. Vetterli, "Spatially adaptive wavelet thresholding with context modelling for image denoising," IEEE Trans. Image Processing, vol. 9, pp. 1522-1531, Sept. 2000.
 H. Choi and R. Baraniuk, "Multiscale texture segmentation using wavelet-domain hidden Markov models," in Proc. Int. Conf. Signals, Syst., Comput., vol. 2, 1998, pp. 1692-1697.
 L. Sendur and I. W. Selesnick, "A bivariate shrinkage function for wavelet-based denoising," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Orlando, May 13-17, 2002.
[Online], Avalilable: http://taco.poly.edu/selesi/bishrink/icassp2002.pdf
 L. Sendur and I. W. Selesnick, "Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency," IEEE Trans. Signal Processing, vol. 50, pp. 2744-2756, Nov. 2002.
 L. Sendur and I. W. Selesnick, "Bivariate shrinkage with local variance estimation," IEEE Signal Processing Letters, vol. 9, pp. 438-441, Dec. 2002.
 R. Grover Brown and P. Y.C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, New York, John Wiley & Sons, Inc, 1992.
 S. Haykin, Adaptive Filter Theory, New Jersey, Prentice-Hall, Inc., 1991.
 S. Haykin, Modern Filters, New York, MacMillan Publishing Company, 1990.
 C. W. Helstrom, Probability and Stochastic Processes for Engineers, New York, MacMillan Publishing Company, 1991.
 S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, New Jersey, Prentice-Hall, Inc., 1993.
 R. E. Kalman, "A new approach to linear filtering and prediction problems", J. Basic Eng., Series 82D, pp.35-45, Mar. 1960.
 F. Argenti and L. Alparone, "Speckle removal from SAr images in the undecimated wavelet domain," IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 2363-2374, Nov. 2002.
 H. Xie, L. E. Pierce, and F. T. Ulaby, "Statistical properties of logarithmically transformed speckle," IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 721-727, Mar. 2002.
 J. W. Goodman, "Some fundamental properties of speckle," Journal Optics Society of America, 66:1145-1150, 1976.
 M. Mastriani and A. Giraldez, "Enhanced Directional Smoothing Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar Images," Journal of Measurement Science Review, vol 4, no.3, pp.1-11, 2004.
[Online], Available: http://www.measurement.sk/2004/S3/Mastriani.pdf
 H.S. Tan. (2001, October). Denoising of Noise Speckle in radar Image.
[Online]. Available: http://innovexpo.itee.uq.edu.au/2001/projects/s804294/thesis.pdf
 Y. Yu, and S.T. Acton, "Speckle Reducing Anisotropic Diffusion," IEEE Trans. Image Processing, vol. 11, pp. 1260-1270, Nov. 2002.
 H. Guo, J.E. Odegard, M. Lang, R.A. Gopinath, I. Selesnick, and C.S. Burrus, "Speckle reduction via wavelet shrinkage with application to SAR based ATD/R," Technical Report CML TR94-02, CML, Rice University, Houston, 1994.
 X.-P. Zhang, "Thresholding Neural Network for Adaptive Noise reduction," IEEE Trans. Neural Networks, vol. 12, pp. 567-584, May 2001.
 D.L. Donoho and I.M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200-1224, 1995.
 D.L. Donoho and I.M. Johnstone, "Ideal spatial adaptation via wavelet shrinkage," Biometrika, vol. 81, pp. 425-455, 1994.
 D.L. Donoho, I.M. Johnstone, G. Kerkyacharian, and D. Picard, "Wavelet shrinkage: asymptopia," Journal of Royal Stat. Soc., vol. 57, no.2, pp. 301-369, 1995.
 D.L. Donoho, I.M. Johnstone, G. Kerkyacharian, and D. Picard, "Density estimation by wavelet thresholding," Annals of Stat., vol. 24, pp. 508-539, 1996.
 D.L. Donoho, "De-Noising by soft-thresholding," IEEE Trans. on Inf. Theory, vol. 41, no. 3, pp. 613-627, 1995.