Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Boundary-adaptive Despeckling : Simulation Study

  • Lee, Sang-Hoon (Department of Industrial Engineering, KyungWon University)
  • Published : 2009.06.28
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Abstract

In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to boundary is estimated using a non-uniformity measurement based on standard deviation of local region. The new scheme has been extensively evaluated using simulation data, and the experimental results show a considerable improvement in despeckling the images that contain speckle.

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References

  1. Aja-Fernandez, S. and C. Alberaola-Lopez, 2006. On the estimation of the coefficient of variation for anisotropic diffusion speckling filtering, IEEE Trans. on Image Proc., 15(9): 2694 - 2701 https://doi.org/10.1109/TIP.2006.877360
  2. Arsenault H. H. and G. April, 1976. Properties of speckle integrated with a finite aperture and logarithmically transformed, J. Opt. Soc. Amer., 66: 1160-1163 https://doi.org/10.1364/JOSA.66.001160
  3. Cullen, C. G., 1972. Matrices and Linear Transformations. Reading, MA: Addison-Wesley
  4. Dainty, J. C., 1984. Laser Speckle and Related Phenomena, Second Enlarged Edition
  5. Frost, V. S., J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, 1982. A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. Pattern Anal. Mach. Intell., 4: 157-165 https://doi.org/10.1109/TPAMI.1982.4767223
  6. Georgii, H. O., 1979. Canonical Gibbs Measure. Berlin, Germany: Springer-Verlag
  7. Goodman, J. W., 1976. Some fundamental properties of speckle, J. Opt. Soc. Amer., 66: 1145-1150 https://doi.org/10.1364/JOSA.66.001145
  8. Kindermann R. and J. L. Snell, 1982. Markov Random Fields and Their Application, Providence, R. I.: Amer. Math. Soc
  9. Krissan. K., C-F. Westin, R. Kikinis, and K. Vosburgh, 2007. Orientee speckle reducing anisotropic diffusion, IEEE Trans. on Image Proc., 16(5): 1412-1424 https://doi.org/10.1109/TIP.2007.891803
  10. Kuan, D. T., A. A. Sawchuk, and P. Chavel, 1985. Adaptive noise smoothing filter for images with signal-dependent noise, IEEE Trans. Pattern Anal. Machine Intell., 7: 165-177 https://doi.org/10.1109/TPAMI.1985.4767641
  11. Lee, J. S., 1986. Speckle suppression and analysis for synthetic aperture radar, Opt. Eng., 25: 656-643
  12. Lee, S-H, 2007a. Speckle Removal of SAR Imagery Using a Point-Jacobian Iteration MAP Estimation, Korean J. Remote Sens., 23(1): 33-42 https://doi.org/10.7780/kjrs.2007.23.1.33
  13. Lee, S-H, 2007b. Adaptive Iterative Despeckling of SAR Imagery, Korean J. Remote Sens., 23(5): 455-464 https://doi.org/10.7780/kjrs.2007.23.5.455
  14. Perona, P. and J. Malik, 1990. Scale space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Machine Intell., 12: 629-639 https://doi.org/10.1109/34.56205
  15. Varga, R. S., 1962. Matrix Iterative Analysis, Englewood Cliffs, NJ: Prentice-Hall
  16. Yu, Y., and S. Acton, 2002. Speckle reduction anisotropic diffusion, IEEE Trans. on Image Proc., 11(11): 1260-1270 https://doi.org/10.1109/TIP.2002.804276