Document Type : Original Research Paper


K.N. Toosi University of Technology, Tehran, Iran


Despite the wide application of SAR images in lineaments extraction, DEM generation and displacements determination, their radiometric quality and interpretability is degraded due to the presence of a multiplicative noise called speckle. Therefore, the enhancement of SAR images is an important step before using them in any application. In this paper, a new image enhancement method tailored to SAR images is proposed. In this method, the logarithmically transformed SAR image is decomposed using the dual-tree complex wavelet transform (DTCWT).In order to effectively extract the wavelet interscale dependencies, the signal component of wavelet coefficients is modeled with an isotropic stable distribution, while the noise component is approximated using an isotropic Gaussian model. A bivariate Bayesian estimator is then designed to effectively remove speckle from noisy coefficients in the complex wavelet domain. Both quantitative and qualitative comparisons of the proposed method with new speckle reduction methods, demonstrate its higher performance in speckle reduction from SAR images


Abdelnour, A. F. & Selesnick, I. W., 2004-Symmetric nearly orthogonal and orthogonal nearly symmetric wavelets, The Arabian Journal of Science and Engineering, 29: 3-16.
Achim, A., Tsakalides, P. & Bezerianos, A., 2001- Novel Bayesian method for speckle removal in medical ultrasound images, IEEE Trans. Medical Imaging, 20: 772-783.
Achim, A., Tsakalides, P. & Bezerianos, A., 2003-SAR image denoising via Bayesian wavelet shrinkage based on Heavy-Tailed modeling, IEEE Trans. Geosci. Remote Sensing, 41: 1773-1784.
Bolter, R., Gelautz, M. & Franz, L., 1996- SAR speckle simulation, International Archives of Photogrammetry and Remote Sensing, 21:20-25.
Dijkerman, R. W. & Mazumdar, R. R., 1994-Wavelet representations of stochastic processes and multiresolution stochastic models, IEEE Trans. Signal Process., 42: 1640-1652.
Donoho, D. L. & Johnstone, I. M., 1994- Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81: 425-455.
Donoho, D. L., 1995- De-Noising by soft-thresholding, IEEE Trans. Info. Theory, 41: 613-627.
Forouzanfar, M., 2007- Improvement of the wavelet domain Bayesian estimator algorithm for despeckling of medical ultrasound images, M. S. thesis, K. N. Toosi Univesity of Technology, Tehran, Iran.
Frost, V. S., Stiles, J. A., Shanmugan, K. S. & Holtzman, J. C., 1982- A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. Pattern Anal. Machine Intell., PAMI-4: 157-165.
Goodman, J. W., 1976- Some fundamental properties of speckle, Journal of Optical Society of America, 66: 1145-1150.
Jain, A. K., 1989- Fundamentals of digital image processing, Prentice-Hall.
Lee, J. S., 1980- Digital Image enhancement and noise filtering by using local statistics, IEEE Trans. Pattern Anal. Machine Intell., PAM1-2: 286-294.
Liu, J. & Moulin, P., 2001- Information-Theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients, IEEE. Trans. Image process., 10: 1647-1658.
Mallat, S., 1998- A wavelet tour of signals processing, Academic Press, 1998.
Nikias, C. L. & Shao, M., 1994- Signal processing with alpha-stable distributions and applications, Chapman and Hall.
Oliver, C. & Quegan, S., 1998- Understanding Synthetic Aperture Radar Images, Boston, MA: Artech House.
Papoulis, A. & Pillai, S. U., 2002- Probability, random variables and stochastic processes, McGraw-Hills.
Park, J. M., Song, W. J. & Pearlman, W. A., 1999- Speckle filtering of SAR images based on adaptive windowing, IEE Proc. Visoan Image Signal Process., 146: 191-197.
Raney, R. K. & Wessels, G. J., 1988- Spatial consideration in SAR speckle simulation, IEEE Trans. Geoscience and remote sensing, 26: 666-672.
Sattar, F., Floreby, L., Salomonsson, G. & Lovstrom, B., 1997- Image Enhancement Based on a Nonlinear Multiscale Method, IEEE Trans. Image process., 6: 888-895.
Selesnic, I. W., Baraniuk, R. G. & Kingsbury, N. G., 2005- The dual-tree complex wavelet transform, IEEE signal processing magazine, 22: 123-151.
Sendur, L. & Selesnick, I. W., 2002- Biavriate shrinkage functions for wavelet-based denoising exploiting interscale dependency, IEEE Trans. Signal Process., 50: 2744-2756.
Sendur, L. & Selesnick, I. W., 2002- Bivariate shrinkage with local variance estimation, IEEE Signal Processing Letters, 9: 438-441.
Shi, K. & Fung, B., 1994- A Comparison of Digital Speckle Filters, Proceedings of IGRASS94, Pasadena, USA.
Ulaby, F. T. & Dobson, M. C., 1989- Handbook of radar scattering statistics for terrain, Artech House.
Valadan-Zoej, M., Abrishami-Moghaddam., H. & Dehghani, M., 2005- An Efficient Algorithm for Speckle Reduction in SAR Images Using Wavelet Transformation, Geosciences Journal, Geological Survey of Iran, 54: 108-113.
Xie, H., Pierce, L. E. & Ulaby, F., 2002- Statistical properties of logarithmically transform speckle, IEEE Trans. Geosci. Remote sensing, 40: 721-727.
Yu, Y. & Acton, S.T., 2002- Speckle reducing anisotropic diffusion, IEEE Trans. Image process., 11: 1260-1270.